Quiz IT certification exam

Question 1

Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

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Accepted Answer

Configure VPC Service Controls in the organization with a perimeter around project A.

Answer

Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.

Answer

Add firewall rules in project A so only traffic from the VPC in project A is permitted.

Answer

Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project.

Question 2

You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

Accepted Answer

Copy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.

Answer

Create an authorized view that contains the CMEK to decrypt the data when accessed.

Answer

Provide the partner organization a copy of your CMEKs to decrypt the data.

Answer

Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.

Question 3

You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

Accepted Answer

1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Answer

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Provide each data engineering team access to the virtual lake.

Answer

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone. 3. Assign permissions to the data engineering teams at the zone level.

Answer

1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Question 4

You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west 3 region You want to follow Google-recommended practices What should you do?

Accepted Answer

Set the constraints/gcp. resourceLocations organization policy constraint to in: europe-west3-locations.

Answer

Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.

Answer

Set the constraints/gcp. resourceLocations organization policy constraint to in:eu-locations.

Answer

Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.

Question 5

You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely. The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data What should you do?Choose 2 answers

Accepted Answer

Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.

Accepted Answer

Enforce access control in the policy tag taxonomy.

Answer

Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.

Answer

Remove the bigquery. dataViewer role from the data analytics team on the authorized datasets.

Answer

Replace the authorized dataset with an authorized view Use row-level security and apply filter_ expression to limit data access.

Question 6

You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA.  You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?

Accepted Answer

Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.

Answer

Use session windows with a 30-mmute gap duration.

Answer

Use session windows with a 15-minute gap duration.

Answer

Use hopping windows with a 15-mmute window, and a thirty-minute period.

Question 7

You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?

Accepted Answer

Create a new BigOuory table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.

Answer

Create a new BigOuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old Bigauery table.

Answer

Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google-managed encryption key.

Answer

Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.

Question 8

You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

Accepted Answer

Access historical data by using time travel in BigQuery.

Answer

Export the data from BigQuery into a new table that excludes the corrupted data.

Answer

Migrate your data to multi-region BigQuery buckets.

Answer

Create a BigQuery table snapshot on a daily basis.

Question 9

You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

Accepted Answer

Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.

Answer

Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.

Answer

Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.

Answer

Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.

Question 10

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?

Accepted Answer

Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

Answer

Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

Answer

Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

Answer

Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data

Question 11

You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

Accepted Answer

When creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery's ML. EVALUATE clause without specifying any transformations on the raw input data.

Answer

When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE

Answer

Use a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.

Answer

Preprocess all data using Dataflow. At prediction time, use BigOuery's ML. EVALUATE clause without specifying any further transformations on the input data.

Question 12

You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. What should you do?Choose 2 answers

Accepted Answer

Increase the maximum number of Dataflow workers by setting maxNumWorkers in PipelineOptions.

Accepted Answer

Increase the number of nodes in the Bigtable cluster.

Answer

Configure your Dataflow pipeline to use local execution.

Answer

Modify your Dataflow pipeline lo use the Flatten transform before writing to Bigtable.

Answer

Modify your Dataflow pipeline to use the CoGrcupByKey transform before writing to Bigtable.

Question 13

One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re-encrypt all of your CMEK-protected Cloud Storage data that used that key. and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK protection in the future. What should you do?

Accepted Answer

Create a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.

Answer

Rotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.

Answer

Create a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.

Answer

Create a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.

Question 14

You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

Accepted Answer

Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.

Answer

Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

Answer

Adopt multi-regional Cloud Storage buckets in your architecture.

Answer

Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

Question 15

You are using Workflows to call an API that returns a 1 KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?

Accepted Answer

Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.

Answer

Invoke a subworkflow in Workflows to apply the logic on your JSON file.

Answer

Create a Cloud Composer environment and run the logic in Cloud Composer.

Answer

Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.

Question 16

You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

Accepted Answer

Schedule a daily BigQuery snapshot of the table.

Answer

Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.

Answer

Schedule a daily copy of the dataset to a backup region.

Answer

Modify ETL job to load the data into both the current and another backup region.

Question 17

Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?

Accepted Answer

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.

Answer

Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.

Answer

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.

Answer

Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.

Question 18

Your organization uses a multi-cloud data storage strategy, storing data in Cloud Storage, and data in Amazon Web Services' (AWS) S3 storage buckets. All data resides in US regions. You want to query up-to-date data by using BigQuery. regardless of which cloud the data is stored in. You need to allow users to query the tables from BigQuery without giving direct access to the data in the storage buckets What should you do?

Accepted Answer

Set up a BigQuery Omni connection to the AWS S3 bucket data. Create external tables over the Cloud Storage and S3 data and query the data using BigQuery directly.

Answer

Set up a BigQuery Omni connection to the AWS S3 bucket data Create BigLake tables over the Cloud Storage and S3 data and query the data using BigQuery directly.

Answer

Use the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets Create BigLake tables over the Cloud Storage data and query the data using BigQuery directly.

Answer

Use the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets Create external tables over the Cloud Storage data and query the data using BigQuery directly

Question 19

You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?

Accepted Answer

Move your data to Cloud Storage. Run your jobs on Dataproc.

Answer

Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.

Answer

Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.

Answer

Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

Question 20

You work for a farming company. You have one BigQuery table named sensors, which is about 500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp. which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

Accepted Answer

1. Create a metrics table partitioned by timestamp. 2. Create a sensorld column in the metrics table, that points to the id column in the sensors table. 3. Use an IHSEW statement every 30 seconds to append new metrics to the metrics table. 4. Join the two tables, if needed, when running the analytical query.

Answer

1. Create a retries column in the sensorstable. 2. Set record type and repeated mode for the metrics column. 3. Use an UPDATE statement every 30 seconds to add new metrics.

Answer

1. Create a metrics column in the sensors table. 2. Set RECORD type and REPEATED mode for the metrics column. 3. Use an INSERT statement every 30 seconds to add new metrics.

Answer

1. Create a metrics table partitioned by timestamp. 2. Create a sensor Id column in the metrics table, that points to the _d column in the sensors table. 3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table. 4. Join the two tables, if needed, when running the analytical query.

Question 21

You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

Accepted Answer

1 Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage 2. Define a BigQuery external table for SQL processing. 3. Use Dataproc Spark to process the Cloud Storage files.

Answer

1. Deploy a long-living Dalaproc cluster with Apache Hive and Ranger enabled. 2. Configure Ranger for column level security. 3. Process with Dataproc Spark or Hive SQL.

Answer

1. Define a BigLake table. 2. Create a taxonomy of policy tags in Data Catalog. 3. Add policy lags to columns. 4. Process with the Spark-BigQuery connector or BigQuery SOL.

Answer

1. Load the data to BigQuery tables. 2. Create a taxonomy of policy tags in Data Catalog. 3. Add policy tags to columns. 4. Procoss with the Spark-BigQuery connector or BigQuery SQL.

Question 22

You are administering shared BigQuery datasets that contain views used by multiple teams in your organization. The marketing team is concerned about the variability of their monthly BigQuery analytics spend using the on-demand billing model. You need to help the marketing team establish a consistent BigQuery analytics spend each month. What should you do?

Accepted Answer

Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.

Answer

Create a BigQuery Standard pay-as-you go reservation with a baseline of 0 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.

Answer

Establish a BigQuery quota for the marketing team, and limit the maximum number of bytes scanned each day.

Answer

Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.

Question 23

You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

Accepted Answer

Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.

Answer

Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable email notifications.

Answer

Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Answer

Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Question 24

You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

Accepted Answer

Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming. batch processing, and Cloud DLP Select BigQuery as your data sink.

Answer

Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.

Answer

Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.

Answer

Set up Datastream to replicate your on-premise data on BigQuery.

Question 25

Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

Accepted Answer

1 Create one lake for each team. Inside each lake, create one zone for each domain. 2. Attach each to the BigQuory datasets created by the individual teams as assets to the respective zone. 3. Direct each domain to manage their own zone's data assets.

Answer

1. Create one lake for each team. Inside each lake, create one zone for each domain. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Have the central data platform team manage all zones' data assets.

Answer

1 Create one lake for each domain. Inside each lake, create one zone for each team. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Direct each domain to manage their own lake's data assets.

Answer

1 Create one lake for each domain. Inside each lake, create one zone for each team. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Have the central data platform team manage all lakes' data assets.

Question 26

You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

Accepted Answer

Set a retention policy. Lock the retention policy.

Answer

Set a retention policy. Set the default storage class to Archive for long-term digital preservation.

Answer

Enable the Object Versioning feature. Add a lifecycle rule.

Answer

Enable the Object Versioning feature. Create a copy in a bucket in a different region.

Question 27

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

Accepted Answer

Create a BigQuery reservation for the project.

Answer

Create a BigQuery reservation for the job.

Answer

Create a BigQuery reservation for the service account running the job.

Answer

Create a BigQuery reservation for the dataset.

Question 28

A web server sends click events to a Pub/Sub topic as messages. The web server includes an event Timestamp attribute in the messages, which is the time when the click occurred. You have a Dataflow streaming job that reads from this Pub/Sub topic through a subscription, applies some transformations, and writes the result to another Pub/Sub topic for use by the advertising department. The advertising department needs to receive each message within 30 seconds of the corresponding click occurrence, but they report receiving the messages late. Your Dataflow job's system lag is about 5 seconds, and the data freshness is about 40 seconds. Inspecting a few messages show no more than 1 second lag between their event Timestamp and publish Time. What is the problem and what should you do?

Accepted Answer

Messages in your Dataflow job are processed in less than 30 seconds, but your job cannot keep up with the backlog in the Pub/Sub subscription. Optimize your job or increase the number of workers to fix this.

Answer

The advertising department is causing delays when consuming the messages. Work with the advertising department to fix this.

Answer

The web server is not pushing messages fast enough to Pub/Sub. Work with the web server team to fix this.

Answer

Messages in your Dataflow job are taking more than 30 seconds to process. Optimize your job or increase the number of workers to fix this.

Question 29

You have a BigQuery dataset named 'customers'. All tables will be tagged by using a Data Catalog tag template named 'gdpr'. The template contains one mandatory field, 'has sensitive data~. with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the 'has sensitive data' field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which 'hass-ensitive-data' is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

Accepted Answer

Create the 'gdpr' tag template with public visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees. group, and assign the bijquery.dataViewer role to the HR group on the tables that contain sensitive data.

Answer

Create the 'gdpr' tag template with private visibility. Assign the bigquery -dataViewer role to the HR group on the tables that contain sensitive data.

Answer

Create the ~gdpr' tag template with private visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Answer

Create the 'gdpr' tag template with public visibility. Assign the bigquery. dataViewer role to the HR group on the tables that contain sensitive data.

Question 30

You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

Accepted Answer

Use Dataform to build, manage, and schedule SQL pipelines.

Answer

Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.

Answer

Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.

Answer

Use Data Fusion to build and execute ETL pipelines

Question 31

You recently deployed several data processing jobs into your Cloud Composer 2 environment. You notice that some tasks are failing in Apache Airflow. On the monitoring dashboard, you see an increase in the total workers' memory usage, and there were worker pod evictions. You need to resolve these errors. What should you do?Choose 2 answers

Accepted Answer

Increase the memory available to the Airflow workers.

Accepted Answer

Increase the maximum number of workers and reduce worker concurrency.

Answer

Increase the directed acyclic graph (DAG) file parsing interval.

Answer

Increase the memory available to the Airflow triggerer.

Answer

Increase the Cloud Composer 2 environment size from medium to large.

Question 32

You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

Accepted Answer

Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.

Answer

Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.

Answer

Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.

Answer

Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

Question 33

You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:* Data engineers, which require lull data lake access* Analytic users, which require access to curated dataYou need to assign access rights to these two groups. What should you do?

Accepted Answer

1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake. 2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.

Answer

1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake. 2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone.

Answer

1. Grant the bigquery.dataownex role on BigQuery datasets and the storage.objectcreator role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.

Answer

1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectviewer role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.

Question 34

Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

Accepted Answer

Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.

Answer

Use an exception handling block in your Data Flow's Doffs code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_jnacked_messages_by_region metric on this new topic.

Answer

Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.

Answer

Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/numessages metric on this snapshot.

Question 35

You are a BigQuery admin supporting a team of data consumers who run ad hoc queries and downstream reporting in tools such as Looker. All data and users are combined under a single organizational project. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring. You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?

Accepted Answer

Use available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.

Answer

Use Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.

Answer

Use slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.

Answer

Use Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.

Question 36

You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

Accepted Answer

Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.

Answer

Import the ORC files lo Bigtable tables for the data scientist team.

Answer

Import the ORC files to BigOuery tables for the data scientist team.

Answer

Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.

Question 37

You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?

Accepted Answer

Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

Answer

Create authorized datasets to publish shared data in the subscribing team's project.

Answer

Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery. dataViewer role on the dataset.

Answer

Use Analytics Hub to facilitate data sharing.

Question 38

You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

Accepted Answer

Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Answer

Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.

Answer

Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word 'street' appears.

Answer

Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Question 39

You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet. What should you do?

Accepted Answer

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on- premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.

Answer

Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the Simba. googlebigqueryodbc. ini tile to point to your data center's NAT gateway.

Answer

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center. Use IP Allovlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.

Answer

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward-SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on-premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.

Question 40

The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?

Accepted Answer

Update SOL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.

Answer

Update SQL pipelines and ad-hoc queries to run as interactive query jobs.

Answer

Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.

Answer

Increase the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.

Google Professional Data Engineer Practice Test 9

Google Professional Data Engineer Practice Tests

Google Professional Data Engineer Practice Test 9

Question

Google Professional Data Engineer Practice Tests

Practice Tests