Google Associate Data Practitioner Practice Exams
Last updated on Oct 21,2026- Exam Code: Associate Data Practitioner
- Exam Name: Google Cloud Associate Data Practitioner ( ADP Exam )
- Certification Provider: Google
- Latest update: Oct 21,2026
Question #61
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline’s performance and be alerted if errors occur.
What should you do?
- A . Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
- B . Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
- C . Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
- D . Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.
Reference: Cloud Monitoring for Dataflow: https://cloud.google.com/dataflow/docs/guides/using-monitoring
Cloud Monitoring: https://cloud.google.com/monitoring/docs
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.
Reference: Cloud Monitoring for Dataflow: https://cloud.google.com/dataflow/docs/guides/using-monitoring
Cloud Monitoring: https://cloud.google.com/monitoring/docs
Question #61
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline’s performance and be alerted if errors occur.
What should you do?
- A . Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
- B . Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
- C . Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
- D . Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.
Reference: Cloud Monitoring for Dataflow: https://cloud.google.com/dataflow/docs/guides/using-monitoring
Cloud Monitoring: https://cloud.google.com/monitoring/docs
A
Explanation:
Comprehensive and Detailed in Depth
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.
Reference: Cloud Monitoring for Dataflow: https://cloud.google.com/dataflow/docs/guides/using-monitoring
Cloud Monitoring: https://cloud.google.com/monitoring/docs
Question #63
You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement, and previous click behavior. After training the model, you want to generate predictions on new data.
Which model type should you use in BigQuery ML?
- A . Linear regression
- B . Matrix factorization
- C . Logistic regression
- D . K-means clustering
Correct Answer: C
C
Explanation:
Comprehensive and Detailed In-Depth
Predicting the likelihood of a click (binary outcome: click or no-click) requires a classification model.
BigQuery ML supports this use case with logistic regression.
Option A: Linear regression predicts continuous values, not probabilities for binary outcomes.
Option B: Matrix factorization is for recommendation systems, not binary prediction.
Option C: Logistic regression predicts probabilities for binary classification (e.g., click likelihood), ideal for this scenario and supported in BigQuery ML.
Option D: K-means clustering is for unsupervised grouping, not predictive modeling.
Extract from Google Documentation: From "BigQuery ML: Logistic Regression"
(https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#logistic_reg): "Logistic regression models are used to predict the probability of a binary outcome, such as whether an event will occur, making them suitable for classification tasks like click prediction."
Reference: Google Cloud Documentation – "BigQuery ML Model Types"
(https://cloud.google.com/bigquery-ml/docs/introduction).
C
Explanation:
Comprehensive and Detailed In-Depth
Predicting the likelihood of a click (binary outcome: click or no-click) requires a classification model.
BigQuery ML supports this use case with logistic regression.
Option A: Linear regression predicts continuous values, not probabilities for binary outcomes.
Option B: Matrix factorization is for recommendation systems, not binary prediction.
Option C: Logistic regression predicts probabilities for binary classification (e.g., click likelihood), ideal for this scenario and supported in BigQuery ML.
Option D: K-means clustering is for unsupervised grouping, not predictive modeling.
Extract from Google Documentation: From "BigQuery ML: Logistic Regression"
(https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#logistic_reg): "Logistic regression models are used to predict the probability of a binary outcome, such as whether an event will occur, making them suitable for classification tasks like click prediction."
Reference: Google Cloud Documentation – "BigQuery ML Model Types"
(https://cloud.google.com/bigquery-ml/docs/introduction).
Question #64
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?
- A . Use Cloud Client Libraries to transfer the data over the internet.
- B . Use the gcloud storage command to transfer the data over the internet.
- C . Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
- D . Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
Correct Answer: D
D
Explanation:
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).
D
Explanation:
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).
Question #64
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?
- A . Use Cloud Client Libraries to transfer the data over the internet.
- B . Use the gcloud storage command to transfer the data over the internet.
- C . Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
- D . Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
Correct Answer: D
D
Explanation:
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).
D
Explanation:
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).
Question #64
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?
- A . Use Cloud Client Libraries to transfer the data over the internet.
- B . Use the gcloud storage command to transfer the data over the internet.
- C . Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
- D . Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
Correct Answer: D
D
Explanation:
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).
D
Explanation:
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).
Question #67
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?
- A . Compare the two different models.
- B . Evaluate the data skewness.
- C . Evaluate data drift.
- D . Compare the confusion matrix.
Correct Answer: C
C
Explanation:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model’s performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.
C
Explanation:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model’s performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.
Question #68
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud. You want to be able to manage the code without needing to provision and manage your own cluster.
What should you do?
- A . Migrate the Spark jobs to Dataproc Serverless.
- B . Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
- C . Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
- D . Migrate the Spark jobs to Dataproc on Compute Engine.
Correct Answer: A
A
Explanation:
Migrating the Spark jobs to Dataproc Serverless is the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.
A
Explanation:
Migrating the Spark jobs to Dataproc Serverless is the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.
Question #69
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status.
What should you do?
- A . Use a Transfer Appliance.
- B . Use Cloud Storage FUSE.
- C . Use Storage Transfer Service.
- D . Use gcloud storage commands.
Correct Answer: C
C
Explanation:
Using the Storage Transfer Service is the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.
C
Explanation:
Using the Storage Transfer Service is the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.
Question #70
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization’s storage costs. You want to use the most efficient approach while minimizing cost.
What should you do?
- A . Create a scheduled query that periodically runs an update statement in SQL that sets the “deleted" column to “yes” for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
- B . Create a view that filters out rows that are older than one year.
- C . Require users to specify a partition filter using the alter table statement in SQL.
- D . Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
Correct Answer: D
D
Explanation:
Setting the table partition expiration period to one year using the ALTER TABLE statement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.
Extract from Google Documentation: From "Managing Partitioned Tables in BigQuery"
(https://cloud.google.com/bigquery/docs/partitioned-tables#expiration): "Set a partition expiration time using ALTER TABLE to automatically remove partitions older than a specified duration, reducing storage costs efficiently for ingestion-time partitioned tables."
Reference: Google Cloud Documentation – "BigQuery Partitioned Tables"
(https://cloud.google.com/bigquery/docs/partitioned-tables).
D
Explanation:
Setting the table partition expiration period to one year using the ALTER TABLE statement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.
Extract from Google Documentation: From "Managing Partitioned Tables in BigQuery"
(https://cloud.google.com/bigquery/docs/partitioned-tables#expiration): "Set a partition expiration time using ALTER TABLE to automatically remove partitions older than a specified duration, reducing storage costs efficiently for ingestion-time partitioned tables."
Reference: Google Cloud Documentation – "BigQuery Partitioned Tables"
(https://cloud.google.com/bigquery/docs/partitioned-tables).