Improve Google Professional Data Engineer Exam Professional-Data-Engineer Study Guides

NEW QUESTION 1

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 2

You are designing a cloud-native historical data processing system to meet the following conditions:
The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
A streaming data pipeline stores new data daily.
Peformance is not a factor in the solution.
The solution design should maximize availability.
How should you design data storage for this solution?

• A. Create a Cloud Dataproc cluster with high availabilit
• B. Store the data in HDFS, and peform analysis as needed.
• C. Store the data in BigQuer
• D. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
• E. Store the data in a regional Cloud Storage bucke
• F. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
• G. Store the data in a multi-regional Cloud Storage bucke
• H. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer: D

NEW QUESTION 3

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country
You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

• A. Users are running too many concurrent queries in the system
• B. The [myproject:mydataset.mytable] table has too many partitions
• C. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
• D. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Answer: A

NEW QUESTION 4

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

• A. Include multiple time series values within the row key
• B. Keep the row keep as an 8 bit integer
• C. Keep your row key reasonably short
• D. Keep your row key as long as the field permits

Answer: C

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION 5

The for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

• A. Cloud Dataflow connector
• B. DataFlow SDK
• C. BiqQuery API
• D. BigQuery Data Transfer Service

Answer: A

Explanation:
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
Reference: https://cloud.google.com/bigtable/docs/dataflow-hbase

NEW QUESTION 6

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

• A. Linear regression
• B. Logistic classification
• C. Recurrent neural network
• D. Feedforward neural network

Answer: A

NEW QUESTION 7

You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

• A. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery
• B. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
• C. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
• D. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery

Answer: A

NEW QUESTION 8

You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

• A. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
• B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
• C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
• D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: B

NEW QUESTION 9

Cloud Dataproc charges you only for what you really use with billing.

• A. month-by-month
• B. minute-by-minute
• C. week-by-week
• D. hour-by-hour

Answer: B

Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.
Reference: https://cloud.google.com/dataproc/docs/concepts/overview

NEW QUESTION 10

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 11

When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

• A. Your gcloud does not have access to the BigQuery resources
• B. BigQuery cannot be accessed from local machines
• C. You are missing gcloud on your machine
• D. Pipelines cannot be run locally

Answer: A

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION 12

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

• A. Set the BigQuery dataset to be regiona
• B. In the event of an emergency, use a point-in-time snapshot to recover the data.
• C. Set the BigQuery dataset to be regiona
• D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
• E. In the event of an emergency, use the backup copy of the table.
• F. Set the BigQuery dataset to be multi-regiona
• G. In the event of an emergency, use a point-in-time snapshot to recover the data.
• H. Set the BigQuery dataset to be multi-regiona
• I. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
• J. In the event of an emergency, use the backup copy of the table.

Answer: B

NEW QUESTION 13

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

• A. Convert all daily log tables into date-partitioned tables
• B. Convert the sharded tables into a single partitioned table
• C. Enable query caching so you can cache data from previous months
• D. Create separate views to cover each month, and query from these views

Answer: A

NEW QUESTION 14

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

• 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

Answer: B

Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data
structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION 15

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 16

You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using Sidelnputs to join data You noticed that the pipeline is taking longer to complete than expected, what should you do to expedite the Dataflow job?

• A. Switch to compressed Avro files
• B. Reduce the batch size
• C. Retry records that throw an error
• D. Use CoGroupByKey instead of the Sidelnput

Answer: B

NEW QUESTION 17

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

• 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

Answer: D

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION 18

You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

• A. Deploy a Kafka cluster on GCE VM Instance
• B. Configure your on-prem cluster to mirror your topics to the cluster running in GC
• C. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
• D. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connecto
• E. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
• F. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connecto
• G. Use a Dataflow job to read fron PubSub and write to GCS.
• H. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connecto
• I. Use a Dataflow job to read fron PubSub and write to GCS.

Answer: A

NEW QUESTION 19

Which Java SDK class can you use to run your Dataflow programs locally?

• A. LocalRunner
• B. DirectPipelineRunner
• C. MachineRunner
• D. LocalPipelineRunner

Answer: B

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION 20

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

Explanation:
Reference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION 21
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