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Professional-Data-Engineer Google Professional Data Engineer Exam Questions and Answers

Questions 4

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

Options:

A.

Create a Google Cloud Dataflow job to process the data.

B.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

C.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

D.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

E.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

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Questions 5

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

Options:

A.

Configure your Cloud Dataflow pipeline to use local execution

B.

Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

C.

Increase the number of nodes in the Cloud Bigtable cluster

D.

Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

E.

Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

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Questions 6

You are creating the CI'CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

Options:

A.

1. Use Cloud Build to build a container and the Kubemetes Pod Operator to deploy the code of the DAG to the Google Kubernetes

Engine (GKE) cluster of the development instance for testing.

2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.

B.

1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.

2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

C.

1 Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing.

2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.

D.

1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.

2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.

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Questions 7

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Options:

A.

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

B.

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

C.

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

D.

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

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Questions 8

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

Options:

A.

1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators

2 Use a single shared DAG for all tables that need to go through the pipeline

3 Schedule the DAG to run hourly

B.

1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.

2 Create a separate DAG for each table that needs to go through the pipeline

3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C.

1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators

2 Create a separate DAG for each table that needs to go through the pipeline

3 Schedule the DAGs to run hourly

D.

1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators

2 Use a single shared DAG for all tables that need to go through the pipeline.

3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

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Questions 9

You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier. 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

Options:

A.

Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity) Modify the Dataflow pipeline and new clients to use all instances

B.

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.

C.

Create a new Memorystore for Memcached instance Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance Delete the old instance.

D.

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and create multiple read replicas Delete the old instance.

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Questions 10

You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

Options:

A.

Use Cloud ML Engine for training existing Spark ML models

B.

Rewrite your models on TensorFlow, and start using Cloud ML Engine

C.

Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

D.

Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

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Questions 11

You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

Options:

A.

Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable email

notifications.

B.

Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to

true.

C.

Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to

true.

D.

Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable

notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.

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Questions 12

Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

Options:

A.

multi-keyed data with very high latency

B.

multi-keyed data with very low latency

C.

single-keyed data with very low latency

D.

single-keyed data with very high latency

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Questions 13

Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Options:

A.

An hourly watermark

B.

An event time trigger

C.

The with Allowed Lateness method

D.

A processing time trigger

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Questions 14

Which of the following is not possible using primitive roles?

Options:

A.

Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.

B.

Give UserA owner access and UserB editor access for all datasets in a project.

C.

Give a user access to view all datasets in a project, but not run queries on them.

D.

Give GroupA owner access and GroupB editor access for all datasets in a project.

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Questions 15

Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Options:

A.

Trigger based on element size in bytes

B.

Trigger that is a combination of other triggers

C.

Trigger based on element count

D.

Trigger based on time

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Questions 16

Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

Options:

A.

Preemptible workers cannot use persistent disk.

B.

Preemptible workers cannot store data.

C.

If a preemptible worker is reclaimed, then a replacement worker must be added manually.

D.

A Dataproc cluster cannot have only preemptible workers.

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Questions 17

Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

Options:

A.

Blaze

B.

Spark

C.

Fire

D.

Ignite

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Questions 18

Which of these sources can you not load data into BigQuery from?

Options:

A.

File upload

B.

Google Drive

C.

Google Cloud Storage

D.

Google Cloud SQL

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Questions 19

What are two of the benefits of using denormalized data structures in BigQuery?

Options:

A.

Reduces the amount of data processed, reduces the amount of storage required

B.

Increases query speed, makes queries simpler

C.

Reduces the amount of storage required, increases query speed

D.

Reduces the amount of data processed, increases query speed

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Questions 20

Which of the following is NOT true about Dataflow pipelines?

Options:

A.

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

B.

Dataflow pipelines can consume data from other Google Cloud services

C.

Dataflow pipelines can be programmed in Java

D.

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

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Questions 21

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

Options:

A.

dataflow.worker

B.

dataflow.compute

C.

dataflow.developer

D.

dataflow.viewer

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Questions 22

In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

Options:

A.

VPN connection

B.

Special browser

C.

SSH tunnel

D.

FTP connection

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Questions 23

If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

Options:

A.

Unsupervised learning

B.

Regressor

C.

Classifier

D.

Clustering estimator

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Questions 24

What are the minimum permissions needed for a service account used with Google Dataproc?

Options:

A.

Execute to Google Cloud Storage; write to Google Cloud Logging

B.

Write to Google Cloud Storage; read to Google Cloud Logging

C.

Execute to Google Cloud Storage; execute to Google Cloud Logging

D.

Read and write to Google Cloud Storage; write to Google Cloud Logging

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Questions 25

Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

Options:

A.

The wide model is used for memorization, while the deep model is used for generalization.

B.

A good use for the wide and deep model is a recommender system.

C.

The wide model is used for generalization, while the deep model is used for memorization.

D.

A good use for the wide and deep model is a small-scale linear regression problem.

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Questions 26

If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

Options:

A.

1 continuous and 2 categorical

B.

3 categorical

C.

3 continuous

D.

2 continuous and 1 categorical

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Questions 27

Which of these statements about exporting data from BigQuery is false?

Options:

A.

To export more than 1 GB of data, you need to put a wildcard in the destination filename.

B.

The only supported export destination is Google Cloud Storage.

C.

Data can only be exported in JSON or Avro format.

D.

The only compression option available is GZIP.

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Questions 28

Which of these is NOT a way to customize the software on Dataproc cluster instances?

Options:

A.

Set initialization actions

B.

Modify configuration files using cluster properties

C.

Configure the cluster using Cloud Deployment Manager

D.

Log into the master node and make changes from there

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Questions 29

By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Options:

A.

Windows at every 100 MB of data

B.

Single, Global Window

C.

Windows at every 1 minute

D.

Windows at every 10 minutes

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Questions 30

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Options:

A.

Do not use a production instance.

B.

Run your test for at least 10 minutes.

C.

Before you test, run a heavy pre-test for several minutes.

D.

Use at least 300 GB of data.

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Questions 31

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

Options:

A.

Disable caching by editing the report settings.

B.

Disable caching in BigQuery by editing table details.

C.

Refresh your browser tab showing the visualizations.

D.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

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Questions 32

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy?

Options:

A.

Grant the consultant the Viewer role on the project.

B.

Grant the consultant the Cloud Dataflow Developer role on the project.

C.

Create a service account and allow the consultant to log on with it.

D.

Create an anonymized sample of the data for the consultant to work with in a different project.

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Questions 33

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

Options:

A.

Check the dashboard application to see if it is not displaying correctly.

B.

Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.

C.

Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

D.

Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.

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Questions 34

Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Options:

A.

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

B.

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

C.

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

D.

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

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Questions 35

Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

Options:

A.

Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.

B.

Use a Cloud SQL for PostgreSQL highly available instance first, and 8»gtable with US. Europe, and Asia

replication alter securing funding

C.

Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.

D.

Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.

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Questions 36

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

Options:

A.

Eliminate features that are highly correlated to the output labels.

B.

Combine highly co-dependent features into one representative feature.

C.

Instead of feeding in each feature individually, average their values in batches of 3.

D.

Remove the features that have null values for more than 50% of the training records.

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Questions 37

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Options:

A.

Continuously retrain the model on just the new data.

B.

Continuously retrain the model on a combination of existing data and the new data.

C.

Train on the existing data while using the new data as your test set.

D.

Train on the new data while using the existing data as your test set.

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Questions 38

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

Options:

A.

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

B.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

C.

Use the NOW () function in BigQuery to record the event’s time.

D.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

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Questions 39

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Options:

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

Create a view on the table to present to the virtualization tool.

D.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

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Questions 40

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Options:

A.

Store the common data in BigQuery as partitioned tables.

B.

Store the common data in BigQuery and expose authorized views.

C.

Store the common data encoded as Avro in Google Cloud Storage.

D.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

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Questions 41

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

Options:

A.

Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

B.

Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.

C.

Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

D.

Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

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Questions 42

MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

Options:

A.

The zone

B.

The number of workers

C.

The disk size per worker

D.

The maximum number of workers

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Questions 43

You need to compose visualizations for operations teams with the following requirements:

Which approach meets the requirements?

Options:

A.

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

B.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

C.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

D.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

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Questions 44

You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

Options:

A.

Run a scheduled query to pull the necessary data at specific intervals daily.

B.

Create a materialized view based off of the query being run.

C.

Use a cached query to accelerate time to results.

D.

Limit the query columns being pulled in the final result.

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Questions 45

You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

Options:

A.

1. Deploy a long-living Dalaproc cluster with Apache Hive and Ranger enabled.

2. Configure Ranger for column level security.

3. Process with Dataproc Spark or Hive SQL.

B.

1. Define a BigLake table.

2. Create a taxonomy of policy tags in Data Catalog.

3. Add policy lags to columns.

4. Process with the Spark-BigQuery connector or BigQuery SOL.

C.

1. Load the data to BigQuery tables.

2. Create a taxonomy of policy tags in Data Catalog.

3. Add policy tags to columns.

4. Procoss with the Spark-BigQuery connector or BigQuery SQL.

D.

1 Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage

2. Define a BigQuery external table for SQL processing.

3. Use Dataproc Spark to process the Cloud Storage files.

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Questions 46

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Options:

A.

Rowkey: date#device_idColumn data: data_point

B.

Rowkey: dateColumn data: device_id, data_point

C.

Rowkey: device_idColumn data: date, data_point

D.

Rowkey: data_pointColumn data: device_id, date

E.

Rowkey: date#data_pointColumn data: device_id

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Questions 47

You need to compose visualization for operations teams with the following requirements:

    Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)

    The report must not be more than 3 hours delayed from live data.

    The actionable report should only show suboptimal links.

    Most suboptimal links should be sorted to the top.

    Suboptimal links can be grouped and filtered by regional geography.

    User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

Options:

A.

Look through the current data and compose a series of charts and tables, one for each possible

combination of criteria.

B.

Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

C.

Export the data to a spreadsheet, compose a series of charts and tables, one for each possible

combination of criteria, and spread them across multiple tabs.

D.

Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

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Questions 48

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients’ personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Options:

A.

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

B.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

C.

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

D.

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

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Questions 49

You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster’s local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

Options:

A.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.

B.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.

C.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

D.

Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables. Replicate external Hive tables to the native ones.

E.

Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.

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Questions 50

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

Options:

A.

Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

B.

Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

C.

Modify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

D.

Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

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Questions 51

You currently use a SQL-based tool to visualize your data stored in BigQuery The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

Options:

A.

Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_3taleness parameter to 4 hours and the enable_refresh parameter to true. Reference the materialized views in the data visualization tool.

B.

Create views for the visualization queries. Reference the views in the data visualization tool.

C.

Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle

changed data automatically. Reference the materialized views in the data visualization tool.

D.

Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler

to trigger the Cloud Function every 4 hours. Reference the parquet files in the data visualization tool.

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Questions 52

Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day’s events. They also want to use streaming ingestion. What should you do?

Options:

A.

Create a table called tracking_table and include a DATE column.

B.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

C.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

D.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

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Questions 53

You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

Options:

A.

Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

B.

Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

C.

Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.

D.

Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

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Questions 54

You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.

You have the following requirements:

    You will batch-load the posts once per day and run them through the Cloud Natural Language API.

    You will extract topics and sentiment from the posts.

    You must store the raw posts for archiving and reprocessing.

    You will create dashboards to be shared with people both inside and outside your organization.

You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

Options:

A.

Store the social media posts and the data extracted from the API in BigQuery.

B.

Store the social media posts and the data extracted from the API in Cloud SQL.

C.

Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.

D.

Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

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Questions 55

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Options:

A.

Redis

B.

HBase

C.

MySQL

D.

MongoDB

E.

Cassandra

F.

HDFS with Hive

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Questions 56

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Options:

A.

Change the processing job to use Google Cloud Dataproc instead.

B.

Manually start the Cloud Dataflow job each morning when you get into the office.

C.

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

D.

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

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Questions 57

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Options:

A.

The CSV data loaded in BigQuery is not flagged as CSV.

B.

The CSV data has invalid rows that were skipped on import.

C.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

D.

The CSV data has not gone through an ETL phase before loading into BigQuery.

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Questions 58

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Options:

A.

Introduce data compression for each file to increase the rate file of file transfer.

B.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

C.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

D.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

E.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

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Questions 59

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Options:

A.

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

B.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

C.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

D.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

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Questions 60

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Options:

A.

Ensure all the tables are included in global dataset.

B.

Ensure each table is included in a dataset for a region.

C.

Adjust the settings for each table to allow a related region-based security group view access.

D.

Adjust the settings for each view to allow a related region-based security group view access.

E.

Adjust the settings for each dataset to allow a related region-based security group view access.

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Questions 61

An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

Options:

A.

Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used

B.

Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

C.

Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

D.

Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

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Questions 62

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Options:

A.

Load the data every 30 minutes into a new partitioned table in BigQuery.

B.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

C.

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

D.

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

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Questions 63

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

    The user profile: What the user likes and doesn’t like to eat

    The user account information: Name, address, preferred meal times

    The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

Options:

A.

BigQuery

B.

Cloud SQL

C.

Cloud Bigtable

D.

Cloud Datastore

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Questions 64

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor= ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Professional-Data-Engineer Question 64

Professional-Data-Engineer Question 64

Options:

A.

Option A

B.

Option B.

C.

Option C

D.

Option D

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Exam Name: Google Professional Data Engineer Exam
Last Update: Dec 7, 2024
Questions: 372

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