What Breathing Professional-Data-Engineer Exam Answers Is

NEW QUESTION 1

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

• A. Use a row key of the form <timestamp>.
• B. Use a row key of the form <sensorid>.
• C. Use a row key of the form <timestamp>#<sensorid>.
• D. Use a row key of the form >#<sensorid>#<timestamp>.

Answer: A

NEW QUESTION 2

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 3

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?

• 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.

Answer: B

NEW QUESTION 4

You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

• A. Use Cloud TPUs without any additional adjustment to your code.
• B. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
• C. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
• D. Stay on CPUs, and increase the size of the cluster you’re training your model on.

Answer: B

NEW QUESTION 5

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=<actorname> 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?

• A. Option A
• B. Option B.
• C. Option C
• D. Option D

Answer: A

NEW QUESTION 6

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 7

You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

• A. ParDo
• B. Sink API
• C. Source API
• D. Data extraction

Answer: A

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION 8

The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

• A. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
• B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
• C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
• D. Import the new records from the CSV file into a new BigQuery tabl
• E. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

Answer: A

NEW QUESTION 9

Which of the following is not possible using primitive roles?

• 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.

Answer: C

Explanation:
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles

NEW QUESTION 10

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 11

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A. Deploy a Cloud Dataproc cluste
• B. Use a standard persistent disk and 50% preemptible worker
• C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• D. Deploy a Cloud Dataproc cluste
• E. Use an SSD persistent disk and 50% preemptible worker
• F. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• G. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
• H. Install the Cloud Storage connector, and store the data in Cloud Storag
• I. Change references in scripts from hdfs:// to gs://
• J. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
• K. Change references in scripts from hdfs:// to gs://

Answer: A

NEW QUESTION 12

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 13

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 14

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: C

NEW QUESTION 15

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

• A. Increase the cluster size with more non-preemptible workers.
• B. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
• C. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
• D. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Answer: D

Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION 16

Which software libraries are supported by Cloud Machine Learning Engine?

• A. Theano and TensorFlow
• B. Theano and Torch
• C. TensorFlow
• D. TensorFlow and Torch

Answer: C

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does

NEW QUESTION 17

Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property= .

• A. details
• B. value
• C. null
• D. id

Answer: B

Explanation:
To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated. The formatting is as follows: file_prefix:property=value.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-properties#formatting

NEW QUESTION 18

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

• A. 500 TB
• B. 1 GB
• C. 1 TB
• D. 500 GB

Answer: C

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options

NEW QUESTION 19

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

• A. Select random samples from the tables using the RAND() function and compare the samples.
• B. Select random samples from the tables using the HASH() function and compare the samples.
• C. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sortin
• D. Compare the hashes of each table.
• E. Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Answer: B

NEW QUESTION 20

You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

• A. Export the information to Cloud Stackdriver, and set up an Alerting policy
• B. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
• C. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
• D. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Answer: B

NEW QUESTION 21
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