The Secret Of Google Professional-Data-Engineer Free Practice Questions

NEW QUESTION 1

You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

• A. cron
• B. Cloud Composer
• C. Cloud Scheduler
• D. Workflow Templates on Cloud Dataproc

Answer: D

NEW QUESTION 2

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

• 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.

Answer: C

Explanation:
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
Reference: https://cloud.google.com/bigquery/docs/exporting-data

NEW QUESTION 3

You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don’t get slots to execute their query and you need to correct this. You’d like to avoid introducing new projects to your account.
What should you do?

• A. Convert your batch BQ queries into interactive BQ queries.
• B. Create an additional project to overcome the 2K on-demand per-project quota.
• C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
• D. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

Answer: C

Explanation:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

NEW QUESTION 4

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

• A. Implement clustering in BigQuery on the ingest date column.
• B. Implement clustering in BigQuery on the package-tracking ID column.
• C. Tier older data onto Cloud Storage files, and leverage extended tables.
• D. Re-create the table using data partitioning on the package delivery date.

Answer: A

NEW QUESTION 5

You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

• A. Deploy small Kafka clusters in your data centers to buffer events.
• B. Have the data acquisition devices publish data to Cloud Pub/Sub.
• C. Establish a Cloud Interconnect between all remote data centers and Google.
• D. Write a Cloud Dataflow pipeline that aggregates all data in session windows.

Answer: A

NEW QUESTION 6

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

• A. Send the data to Google Cloud Datastore and then export to BigQuery.
• B. Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
• C. Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
• D. Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Answer: B

NEW QUESTION 7

What are all of the BigQuery operations that Google charges for?

• A. Storage, queries, and streaming inserts
• B. Storage, queries, and loading data from a file
• C. Storage, queries, and exporting data
• D. Queries and streaming inserts

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION 8

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

• 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

Answer: A

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way
Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION 9

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

• A. Use Cloud Dataproc to run your transformation
• B. Monitor CPU utilization for the cluste
• C. Resize the number of worker nodes in your cluster via the command line.
• D. Use Cloud Dataproc to run your transformation
• E. Use the diagnose command to generate an operational output archiv
• F. Locate the bottleneck and adjust cluster resources.
• G. Use Cloud Dataflow to run your transformation
• H. Monitor the job system lag with Stackdrive
• I. Use the default autoscaling setting for worker instances.
• J. Use Cloud Dataflow to run your transformation
• K. Monitor the total execution time for a sampling of job
• L. Configure the job to use non-default Compute Engine machine types when needed.

Answer: B

NEW QUESTION 10

Your United States-based company has created an application for assessing and responding to user actions. The primary table’s data volume grows by 250,000 records per second. Many third parties use your application’s APIs to build the functionality into their own frontend applications. Your application’s APIs should comply with the following requirements:
Single global endpoint
ANSI SQL support
Consistent access to the most up-to-date data What should you do?

• A. Implement BigQuery with no region selected for storage or processing.
• B. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
• C. Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.
• D. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

Answer: B

NEW QUESTION 11

You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

• A. BigQuery
• B. Cloud Bigtable
• C. Cloud Datastore
• D. Cloud SQL for PostgreSQL

Answer: A

NEW QUESTION 12

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

• A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.Deploy the models using Cloud Datapro
• B. Call the model from your application.
• C. Build and train a classification model with Spark MLlib to generate label
• D. Build and train a second classification model with Spark MLlib to filter results to match customer preference
• E. Deploy theModels using Cloud Datapro
• F. Call the models from your application.
• G. Build an application that calls the Cloud Video Intelligence API to generate label
• H. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.
• I. Build an application that calls the Cloud Video Intelligence API to generate label
• J. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.

Answer: C

NEW QUESTION 13

Which action can a Cloud Dataproc Viewer perform?

• A. Submit a job.
• B. Create a cluster.
• C. Delete a cluster.
• D. List the jobs.

Answer: D

Explanation:
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
Reference: https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_operations_summary

NEW QUESTION 14

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

• A. Re-write the application to load accumulated data every 2 minutes.
• B. Convert the streaming insert code to batch load for individual messages.
• C. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
• D. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Answer: A

NEW QUESTION 15

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
• B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Answer: D

NEW QUESTION 16

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

• A. VPN connection
• B. Special browser
• C. SSH tunnel
• D. FTP connection

Answer: C

Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
Reference:
https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces

NEW QUESTION 17

To give a user read permission for only the first three columns of a table, which access control method would you use?

• A. Primitive role
• B. Predefined role
• C. Authorized view
• D. It's not possible to give access to only the first three columns of a table.

Answer: C

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them
read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference: https://cloud.google.com/bigquery/docs/views#authorized-views

NEW QUESTION 18

Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

• A. Use Google Stackdriver Audit Logs to review data access.
• B. Get the identity and access management IIAM) policy of each table
• C. Use Stackdriver Monitoring to see the usage of BigQuery query slots.
• D. Use the Google Cloud Billing API to see what account the warehouse is being billed to.

Answer: C

NEW QUESTION 19

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

• A. BigQuery
• B. Cloud SQL
• C. Cloud BigTable
• D. Cloud Datastore

Answer: C

NEW QUESTION 20

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? Choose 2 answers.

• A. Review Key Visualizer metric
• B. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
• C. Review Key Visualizer metric
• D. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
• E. Monitor the latency of write operation
• F. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
• G. Monitor storage utilizatio
• H. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
• I. Monitor latency of read operation
• J. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

Answer: AC

NEW QUESTION 21
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