Google Associate Data Practitioner Google Cloud Associate Data Practitioner Exam Practice Test

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Question 1

You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

AEnable access control by using IAM roles.

BUpdate dataset privileges by using the SQL GRANT statement.

CExport the data to Cloud Storage, and use signed URLs to authorize access.

DEncrypt the data by using customer-managed encryption keys (CMEK).

Answer : A

Comprehensive and Detailed In-Depth

BigQuery uses IAM for access control, adhering to least privilege by granting only necessary permissions.

Option A: IAM roles (e.g., roles/bigquery.dataViewer for read-only) restrict query access to authorized users, aligning with Google's security best practices.

Option B: BigQuery doesn't support SQL GRANT for dataset privileges; access is managed via IAM or authorized views.

Option C: Exporting to Cloud Storage with signed URLs bypasses BigQuery's native controls and adds complexity.

Option D: CMEK encrypts data at rest but doesn't control query access. Extract from Google Documentation: From 'Controlling Access to BigQuery' (https://cloud.google.com/bigquery/docs/access-control): 'Use IAM to manage access to BigQuery resources. Predefined roles like roles/bigquery.dataViewer allow you to grant query access while following the principle of least privilege.' Reference: Google Cloud Documentation - 'BigQuery IAM' (https://cloud.google.com/bigquery/docs/access-control).

Extract from Google Documentation: From 'Controlling Access to BigQuery' (https://cloud.google.com/bigquery/docs/access-control): 'Use IAM to manage access to BigQuery resources. Predefined roles like roles/bigquery.dataViewer allow you to grant query access while following the principle of least privilege.'

Question 2

You work for a gaming company that collects real-time player activity dat

a. This data is streamed into Pub/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. You need to design a pipeline that ensures low latency and high throughput while following a Google-recommended approach. What should you do?

AUse Cloud Composer to orchestrate a workflow that reads the data from Pub/Sub, processes the data using a Python script, and writes it to BigQuery.

BUse Dataproc to create an Apache Spark streaming job that reads the data from Pub/Sub, processes the data, and writes it to BigQuery.

CUse Dataflow to create a streaming pipeline that reads the data from Pub/Sub, processes the data, and writes it to BigQuery using the streaming API.

DUse Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming API.

Answer : C

Comprehensive and Detailed in Depth

Why C is correct:Dataflow is the recommended service for real-time stream processing on Google Cloud.

It provides scalable and reliable processing with low latency and high throughput.

Dataflow's streaming API is optimized for Pub/Sub integration and BigQuery streaming inserts.

Why other options are incorrect:A: Cloud Composer is for batch orchestration, not real-time streaming.

B: Dataproc and Spark streaming are more complex and not as efficient as Dataflow for this task.

D: Cloud Run functions are for stateless, event-driven applications, not continuous stream processing.

Dataflow Streaming: https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines

Pub/Sub to BigQuery with Dataflow: https://cloud.google.com/dataflow/docs/tutorials/pubsub-to-bigquery

Question 3

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address

the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

ADesign a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

BDesign the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

CDesign the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

DDesign the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

Answer : D

Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:

Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.

Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.

Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.

Question 4

You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

AUse Cloud Client Libraries to transfer the data over the internet.

BUse the gcloud storage command to transfer the data over the internet.

CCompress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.

DRequest a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Answer : D

Comprehensive and Detailed In-Depth

Transferring 300 TB over a 100 Mbps connection would take an impractical amount of time (over 300 days at theoretical maximum speed, ignoring real-world constraints like latency). Google Cloud provides the Transfer Appliance for large-scale, time-sensitive transfers.

Option A: Cloud Client Libraries over the internet would be slow and unreliable for 300 TB due to bandwidth limitations.

Option B: The gcloud storage command is similarly constrained by internet speed and not designed for such large transfers.

Option C: Compressing and splitting across multiple providers adds complexity and isn't a Google-supported method for Cloud Storage ingestion.

Option D: The Transfer Appliance is a physical device shipped to your location, capable of handling terabytes of data quickly (e.g., 300 TB in days via high-speed local copy), then shipped back to Google for upload to Cloud Storage. Extract from Google Documentation: From 'Transferring Data to Google Cloud Storage' (https://cloud.google.com/storage/docs/transferring-data): 'For transferring large amounts of data (hundreds of terabytes or more), consider using the Transfer Appliance, a high-capacity storage server that you load with data and ship to a Google data center. This is ideal when transferring over the internet would take too long.' Reference: Google Cloud Documentation - 'Transfer Appliance Overview' (https://cloud.google.com/transfer-appliance).

Extract from Google Documentation: From 'Transferring Data to Google Cloud Storage' (https://cloud.google.com/storage/docs/transferring-data): 'For transferring large amounts of data (hundreds of terabytes or more), consider using the Transfer Appliance, a high-capacity storage server that you load with data and ship to a Google data center. This is ideal when transferring over the internet would take too long.'

Question 5

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

AUse the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

BUse BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

CUse Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

DUse Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Answer : B

Using BigQuery to batch load the data and perform cleaning and analysis with SQL is the best approach for this scenario. BigQuery provides powerful SQL capabilities to handle missing values, enforce correct data types, and remove duplicates efficiently. This method simplifies the pipeline by leveraging BigQuery's built-in processing power for both cleaning and analysis, reducing the need for additional tools or services and minimizing complexity.

Question 6

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

AOption A

BOption B

COption C

DOption D

Answer : B

In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should:

Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input.

Rename the target label column to label, as BigQuery ML requires the target column to be named label.

The chosen query satisfies these requirements:

SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label.

The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained.

This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.

Question 7

You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial dat

a. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

AConfigure the Cloud SQL for PostgreSQL instance for multi-region backup locations.

BConfigure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.

CConfigure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.

DConfigure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.

Answer : A

Comprehensive and Detailed In-Depth

The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:

Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post-disaster. Primary operations remain in one zone, minimizing latency.

Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour---too high for valuable data. Manual backup management adds overhead.

Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.

Option D: Asynchronous replication to another region reduces RPO (e.g., seconds) but increases primary write latency (waiting for async confirmation) and doesn't guarantee zero data loss in a crash. Why A is Best: Multi-region backups balance low RPO (continuous logs + frequent backups) with no latency impact on primary operations (single-zone instance). For example, enabling backups to us-east1 from a us-west1 instance ensures recoverability without affecting performance, prioritizing RPO as requested. Extract from Google Documentation: From 'Cloud SQL Backup and Recovery' (https://cloud.google.com/sql/docs/postgres/backup-recovery): 'Configure multi-region backups to store data in a different region, ensuring recoverability after a regional failure with minimal RPO using automated backups and transaction logs, while keeping primary operations low-latency.' Reference: Google Cloud Documentation - 'Cloud SQL Backups' (https://cloud.google.com/sql/docs/postgres/backup-recovery).

Why A is Best: Multi-region backups balance low RPO (continuous logs + frequent backups) with no latency impact on primary operations (single-zone instance). For example, enabling backups to us-east1 from a us-west1 instance ensures recoverability without affecting performance, prioritizing RPO as requested.

Extract from Google Documentation: From 'Cloud SQL Backup and Recovery' (https://cloud.google.com/sql/docs/postgres/backup-recovery): 'Configure multi-region backups to store data in a different region, ensuring recoverability after a regional failure with minimal RPO using automated backups and transaction logs, while keeping primary operations low-latency.'