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Associate-Data-Practitioner Google Cloud Associate Data Practitioner (ADP Exam) Questions and Answers

Questions 4

Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner’s data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices. What should you do?

Options:

A.

Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.

B.

Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.

C.

Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

D.

Grant the partners the bigquery.user IAM role on the BigQuery project.

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Questions 5

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?

A)

Associate-Data-Practitioner Question 5

B)

Associate-Data-Practitioner Question 5

C)

Associate-Data-Practitioner Question 5

D)

Associate-Data-Practitioner Question 5

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

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Questions 6

Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of data. You also want tocreate a reusable framework in case you need to share this data with other teams in the future. What should you do?

Options:

A.

Create authorized views in the team’s Google Cloud project that is only accessible by the team.

B.

Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

C.

Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.

D.

Export the dataset to a Cloud Storage bucket in the team’s Google Cloud project that is only accessible by the team.

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Questions 7

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?

Options:

A.

Design 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.

B.

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

C.

Design 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.

D.

Design 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.

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Questions 8

You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

Options:

A.

Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.

B.

Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

C.

Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

D.

Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

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Questions 9

You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. 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?

Options:

A.

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

B.

Configure 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.

C.

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

D.

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

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Questions 10

You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

Options:

A.

Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

B.

Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.

C.

Use Dataflow to implement a streaming pipeline using anOBJECT_FINALIZEnotification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.

D.

Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create anOBJECT_FINALIZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.

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Questions 11

You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of data. What should you do?

Options:

A.

Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

B.

Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

C.

Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

D.

Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.

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Questions 12

You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

Options:

A.

Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

B.

Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

C.

Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.

D.

Store all data in a single BigQuery table without partitioning or lifecycle policies.

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Questions 13

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

Options:

A.

Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

B.

Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

C.

Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

D.

Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

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Questions 14

Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

Options:

A.

Use Cloud Scheduler to schedule the jobs to run.

B.

Use Cloud Tasks to schedule and run the jobs asynchronously.

C.

Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

D.

Create directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

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Questions 15

Your organization’s ecommerce website collects user activity logs using a Pub/Sub topic. Your organization’s leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

Options:

A.

Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

B.

Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.

C.

Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

D.

Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

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Questions 16

Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data. What should you do?

Options:

A.

Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

B.

Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

C.

Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.

D.

Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

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Questions 17

Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

Options:

A.

Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

B.

Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

C.

Use the existing data pipeline tool’s BigQuery connector to reconfigure the data mapping.

D.

Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

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Questions 18

You work for a financial services company that handles highly sensitive data. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?

Options:

A.

Use customer-supplied encryption keys (CSEK).

B.

Use a dedicated third-party key management system (KMS) chosen by the company.

C.

Use Google-managed encryption keys (GMEK).

D.

Use customer-managed encryption keys (CMEK).

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Questions 19

Your organization plans to move their on-premises environment to Google Cloud. Your organization’s network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

Options:

A.

Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

B.

Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.

C.

Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.

D.

Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

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Questions 20

Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular. You need to design a storage system that is simple and cost-effective. What should you do?

Options:

A.

Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.

B.

Create a single-region bucket with Autoclass enabled.

C.

Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.

D.

Create a single-region bucket with Archive as the default storage class.

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Questions 21

You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

Options:

A.

Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

B.

Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

C.

Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

D.

Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

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Questions 22

Your organization’s business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.

You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

Options:

A.

Disable BiqQuery query result caching.

B.

Modify the schema to use parameterized data types.

C.

Create a scheduled query to calculate and store intermediate results.

D.

Create materialized views.

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Questions 23

You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

Options:

A.

Use BigQuery ML to create a logistic regression model for purchase prediction.

B.

Use Vertex AI Workbench to develop a custom model for purchase prediction.

C.

Use Colab Enterprise to develop a custom model for purchase prediction.

D.

Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

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Questions 24

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?

Options:

A.

Linear regression

B.

Matrix factorization

C.

Logistic regression

D.

K-means clustering

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Questions 25

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Options:

A.

Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

B.

Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

C.

Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

D.

Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

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Questions 26

Your team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

Options:

A.

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation.

B.

Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation.

C.

Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation.

D.

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.

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Exam Name: Google Cloud Associate Data Practitioner (ADP Exam)
Last Update: Apr 2, 2025
Questions: 106

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