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?

Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery

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.

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

Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery

You are building a teal-lime prediction engine that streams files, which may contain Pll (personal identifiable information) data, into Cloud Storage and eventually into BigQuery You want to ensure that the sensitive data is masked but still maintains referential Integrity, because names and emails are often used as join keys How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the Pll data is not accessible by unauthorized individuals?

Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the nontokenized data in a locked-down button.

Redact all Pll data, and store a version of the unredacted data in a locked-down bucket

Scan every table in BigQuery, and mask the data it finds that has Pll

Create a pseudonym by replacing Pll data with a cryptographic format-preserving token

Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

Materialize the dimensional data in views

Partition the data by transaction date

You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

Create one unique table for all of the indices, and then use the index and timestamp as the row key design

Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.

For each index, have a separate table and use a timestamp as the row key design

For each index, have a separate table and use a reverse timestamp as the row key design

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?

Switch to compressed Avro files

Retry records that throw an error

Use CoGroupByKey instead of the Sidelnput

You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API Following Google's best practices, you have both a staging and a production table for the data How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?

Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes

Have a staging table that is an append-only model, and then update the production table every three hours with the changes written to staging

Have a staging table that is an append-only model, and then update the production table every ninety minutes with the changes written to staging

Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every three hours

Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes

You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud.The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates

BigQuery Data Transfer Service for the migration, Pub/Sub and Dataproc for the real-time updates

gsutil for the migration; Pub/Sub and Dataflow for the real-time updates

gsutil for both the migration and the real-time updates

You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?

Call out to the service via HTTP

Create the pipeline statically in the class definition

Create a new object in the startBundle method of DoFn

Batch the job into ten-second increments

You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery In your current relational database, the author information is kept in a separate table and joined to the book information on a common key Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

Create a table that includes information about the books and authors, but nest the author fields inside the author column

Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today

Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

Create a table that includes information about the books and authors, but nest the author fields inside the author column

Keep the schema the same, create a view that joins all of the tables, and always query the view

You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage

Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.

Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage

An aerospace company uses a proprietary data format to store its night dat a. 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?

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

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

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

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

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

An aerospace company uses a proprietary data format to store its night dat a. 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?

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

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

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

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

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

You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat a. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?

Use BigQuery BI Engine with streaming data.

Use BigQuery BI Engine with materialized views

Use BigQuery BI Engine with streaming data.

Use BigQuery Bl Engine with authorized views

Use BigQuery Bl Engine with logical reviews

You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through Al models What should you do?

Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets

Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink

Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery

The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project

Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level

A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard -32) takes two days to complete framing. The model has custom TensorFlow operations that must run partially on a CPU You want to reduce the training time in a cost-effective manner. What should you do?

Train the model using a VM with a GPU hardware accelerator

Change the VM type to n2-highmem-32

Change the VM type to e2 standard-32

Train the model using a VM with a GPU hardware accelerator

Train the model using a VM with a TPU hardware accelerator

An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

Write an application that makes a queue in a NoSQL database

Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.

Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.

Write an application that makes a queue in a NoSQL database

Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic

You are designing a pipeline that publishes application events to a Pub/Sub topic. You need to aggregate events across hourly intervals before loading the results to BigQuery for analysis. Your solution must be scalable so it can process and load large volumes of events to BigQuery. What should you do?

Create a streaming Dataflow job to continually read from the Pub/Sub topic and perform the necessary aggregations using tumbling windows

Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub-Sub topic and performing the necessary aggregations

Schedule a Cloud Function to run hourly, pulling all avertable messages from the Pub/Sub topic and performing the necessary aggregations

Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.

Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do?Choose 2 answers

Periodically DELETE outdated records from the reporting tablePeriodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.

Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.

Periodically DELETE outdated records from the reporting tablePeriodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

Insert each new CDC record and corresponding operation type into a staging table in real time

Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.

You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?

Switch from HODs to SSDs override the preemptible VMs configuration to increase the boot disk size

Increase the see of your parquet files to ensure them to be 1 GB minimum

Switch to TFRecords format (appr 200 MB per We) instead of parquet files

Switch from HDDs to SSDs. copy initial data from Cloud Storage to Hadoop Distributed File System (HDFS) run the Spark job and copy results back to Cloud Storage

Your company currently runs a large on-premises cluster using Spark Hive and Hadoop Distributed File System (HDFS) in a colocation facility. The duster is designed to support peak usage on the system, however, many jobs are batch n nature, and usage of the cluster fluctuates quite dramatically.Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more servers offerings m order to take advantage of the cloud Because of the tuning of their contract renewal with the colocation facility they have only 2 months for their initial migration How should you recommend they approach thee upcoming migration strategy so they can maximize their cost savings in the cloud will still executing the migration in time?

Modernize the Spark workload for Dataflow and the Hive workload for BigQuery

Migrate the workloads to Dataproc plus HOPS, modernize later

Migrate the workloads to Dataproc plus Cloud Storage modernize later

Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery

Modernize the Spark workload for Dataflow and the Hive workload for BigQuery

You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query: You want to optimize your queries for cost and performance. How should you structure your data?

Cluster table data by create_date location_id and device_version

Partition table data by create_date, location_id and device_version

Partition table data by create_date cluster table data by tocation_id and device_version

Cluster table data by create_date location_id and device_version

Cluster table data by create_date, partition by location and device_version

You want to optimize your queries for cost and performance. How should you structure your data?

Partition table data by create_date cluster table data by location_Id and device_version

Partition table data by create_date, location_id and device_version

Partition table data by create_date cluster table data by location_Id and device_version

Cluster table data by create_date location_id and device_version

Cluster table data by create_date partition by locationed and device_version

A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open. After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludesD Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas

Create a Memorystore instance with a high availability (HA) configuration

Write votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery

Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludesD Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas

You are updating the code for a subscriber to a Put/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. You subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to redeliver messages that became available after the snapshot was created

Use Cloud Build for your deployment if an error occurs after deployment, use a Seek operation to locate a tmestamp logged by Cloud Build at the start of the deployment

Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to redeliver messages that became available after the snapshot was created

Set up the Pub/Sub emulator on your local machine Validate the behavior of your new subscriber togs before deploying it to production

Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successful acknowledged if an error occurs after deployment, re-deliver any messages captured by the deadletter queue

Government regulations in the banking industry mandate the protection of client's personally identifiable information (PII). Your company requires PII to be access controlled encrypted and compliant with major data protection standards In addition to using Cloud Data Loss Prevention (Cloud DIP) you want to follow Google-recommended practices and use service accounts to control access to PII. What should you do?

Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group

Assign the required identity and Access Management (IAM) roles to every employee, and create a single service account to access protect resources

Use one service account to access a Cloud SQL database and use separate service accounts for each human user

Use Cloud Storage to comply with major data protection standards. Use one service account shared by all users

Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group

You are migrating a table to BigQuery and are deeding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID and the city and state in which the store is located You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state city and individual store. You want to model this table to minimize query time and cost. What should you do?

Top-level cluster by stale first, then city then store

Partition by transaction time; cluster by state first, then city then store ID

Partition by transaction tome cluster by store ID first, then city, then stale

Top-level cluster by stale first, then city then store

Top-level cluster by store ID first, then city then state.

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?

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

You have an Oracle database deployed in a VM as part of a Virtual Private Cloud (VPC) network. You want to replicate and continuously synchronize 50 tables to BigQuery. You want to minimize the need to manage infrastructure. What should you do?

Create a Datastream service from Oracle to BigQuery, use a private connectivity configuration to the same VPC network, and a connection profile to BigQuery.

Create a Pub/Sub subscription to write to BigQuery directly Deploy the Debezium Oracle connector to capture changes in the Oracle database, and sink to the Pub/Sub topic.

Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle Change Data Capture (CDC), and Dataflow to stream the Kafka topic to BigQuery. D O Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle change data capture (CDC), and the Kafka Connect Google BigQuery Sink Connector.

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?

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

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

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

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

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

You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.

Log debug information in each ParDo function, and analyze the logs at execution time.

Insert output sinks after each key processing step, and observe the writing throughput of each block.

Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks

Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Publish the data that each department shared in Analytics Hub. 4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

1Create a project for storage of the data for your organization. 2 Create a central Cloud Storage bucket with three folders to store the files for each department. 3. Create a central BigQuery dataset with tables prefixed with the department name. 4 Give viewer rights for the storage project for the users of your departments.

1Create a project for storage of the data for each of your departments. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Create user groups for authorized readers for each bucket and dataset. 4 Enable the IT team to administer the user groups to add or remove users as the departments' request.

1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Publish the data that each department shared in Analytics Hub. 4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones. 4 Enable each department to own and share the data of their data lakes.

You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.

Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.

Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.

Store your data in BigQuery. Use the metric as a primary key.

You have a variety of files in Cloud Storage that your data science team wants to use in their models Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.

Load the data into BigQuery and use SQL to transform the data as necessary Provide the data science team access to staging tables to explore the raw data.

Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.

Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.

Create an external table in BigQuery and use SQL to transform the data as necessary Provide the data science team access to the external tables to explore the raw data.

You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Load files from AWS and Azure to Cloud Storage with Cloud Shell gautil rsync arguments.

Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.

Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.

Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.

Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.

Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.

Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

Test and evaluate your model on your curated data to determine how well the model performs.

Use your model to run predictions on fresh customer input data.

Test and evaluate your model on your curated data to determine how well the model performs.

Monitor your model performance, and make any adjustments needed.

Delineate what data will be used for testing and what will be used for training the model.

You need to modernize your existing on-premises data strategy. Your organization currently uses.* Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.* Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.

Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.

Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..

You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

Use Dataflow Prime, and enable Right Fitting to increase the worker resources.

Update the job to increase the maximum number of workers.

Enable Vertical Autoscaling to let the pipeline use larger workers.

Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Google Professional Data Engineer Practice Test 7 - Free Online Exam Questions 241-280

You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app You have reviewed old chat logs and lagged each conversation for intent based on each customer's stated intention for contacting customer service About 70% of customer requests are simple requests that are solved within 10 intents The remaining 30% of inquiries require much longer, more complicated requests Which intents should you automate first?

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Google Professional Data Engineer Practice Test 7

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Google Professional Data Engineer Practice Tests

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