How Many Questions Of Professional-Data-Engineer Exams

NEW QUESTION 1

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?

• A. The zone
• B. The number of workers
• C. The disk size per worker
• D. The maximum number of workers

Answer: A

NEW QUESTION 2

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

• A. Hive
• B. Pig
• C. YARN
• D. Spark

Answer: ABD

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run

NEW QUESTION 3

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?

• 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.

Answer: D

NEW QUESTION 4

The Dataflow SDKs have been recently transitioned into which Apache service?

• A. Apache Spark
• B. Apache Hadoop
• C. Apache Kafka
• D. Apache Beam

Answer: D

Explanation:
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/

NEW QUESTION 5

What are two of the characteristics of using online prediction rather than batch prediction?

• A. It is optimized to handle a high volume of data instances in a job and to run more complex models.
• B. Predictions are returned in the response message.
• C. Predictions are written to output files in a Cloud Storage location that you specify.
• D. It is optimized to minimize the latency of serving predictions.

Answer: BD

Explanation:
Online prediction
Optimized to minimize the latency of serving predictions. Predictions returned in the response message.
Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models. Predictions written to output files in a Cloud Storage location that you specify.
Reference:
https://cloud.google.com/ml-engine/docs/prediction-overview#online_prediction_versus_batch_prediction

NEW QUESTION 6

Does Dataflow process batch data pipelines or streaming data pipelines?

• A. Only Batch Data Pipelines
• B. Both Batch and Streaming Data Pipelines
• C. Only Streaming Data Pipelines
• D. None of the above

Answer: B

Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/

NEW QUESTION 7

You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
Each department should have access only to their data.
Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
Each department has data analysts who need to be able to query but not modify data.
How should you set access to the data in BigQuery?

• A. Create a dataset for each departmen
• B. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
• C. Create a dataset for each departmen
• D. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
• E. Create a table for each departmen
• F. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.
• G. Create a table for each departmen
• H. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

Answer: D

NEW QUESTION 8

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

• A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
• B. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
• C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
• D. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

Answer: D

NEW QUESTION 9

Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

• A. Field promotion
• B. Randomization
• C. Salting
• D. Hashing

Answer: A

Explanation:
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION 10

Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

• A. Migrate the workload to Google Cloud Dataflow
• B. Use pre-emptible virtual machines (VMs) for the cluster
• C. Use a higher-memory node so that the job runs faster
• D. Use SSDs on the worker nodes so that the job can run faster

Answer: A

NEW QUESTION 11

You’re using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You’ve recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?

• A. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
• B. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
• C. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
• D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

Answer: B

NEW QUESTION 12

You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

• A. Cancel
• B. Drain
• C. Stop
• D. Finish

Answer: B

Explanation:
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow
service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.
Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline

NEW QUESTION 13

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 14

You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

• A. Cloud Scheduler
• B. Cloud Dataflow
• C. Cloud Functions
• D. Cloud Composer

Answer: A

NEW QUESTION 15

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

• A. Add a SideInput that returns a Boolean if the element is corrupt.
• B. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
• C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
• D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Answer: B

NEW QUESTION 16

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 17

Which of these is not a supported method of putting data into a partitioned table?

• A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
• B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
• C. Create a partitioned table and stream new records to it every day.
• D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".

Answer: D

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION 18

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

Answer: A

NEW QUESTION 19

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

• A. Use Cloud Dataprep to find null values in sample source dat
• B. Convert all nulls to ‘none’ using a Cloud Dataproc job.
• C. Use Cloud Dataprep to find null values in sample source dat
• D. Convert all nulls to 0 using a Cloud Dataprep job.
• E. Use Cloud Dataflow to find null values in sample source dat
• F. Convert all nulls to ‘none’ using a Cloud Dataprep job.
• G. Use Cloud Dataflow to find null values in sample source dat
• H. Convert all nulls to using a custom script.

Answer: C

NEW QUESTION 20

You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

• A. Create a cron schedule in Cloud Dataprep.
• B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
• C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
• D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Answer: C

NEW QUESTION 21

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?

• 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

Answer: A

NEW QUESTION 22
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