Google Associate Data Practitioner Exam Free Dumps

dumps2025-03-17T10:00:55+08:00

Google Associate Data Practitioner exam secures and manages data on Google Cloud. There are 50-60 multiple choice and multiple select questions in real Google Associate Data Practitioner exam, and you have 2 hours to answer all the questions. The exam language is English. The registration fee is $125. The recommend experience is 6+ months of experience working with data on Google Cloud. We provide the latest Google Associate Data Practitioner exam dumps, which can help you pass the test easily. Free Google Associate Data Practitioner exam dumps are available below.

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1. 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)

Option A

Option B

Option C

Option D

2. Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store.

Which query should you use?

A)

Option A

Option B

Option C

Option D

3. Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations.

What should you do?

Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.

Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.

Use the “Pub/Sub to BigQuery” Dataflow template with a UDF, and write the results to BigQuery.

Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.

4. You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues.

What should you do?

Create a batch pipeline in Cloud Data Fusion by using a Cloud Storage source and a BigQuery sink.

Load the CSV file as a table in BigQuery, and use scheduled queries to run SQL transformation scripts.

Load the CSV file as a table in BigQuery. Create a batch pipeline in Cloud Data Fusion by using a BigQuery source and sink.

Create a batch pipeline in Dataflow by using the Cloud Storage CSV file to BigQuery batch template.

5. You manage a Cloud Storage bucket that stores temporary files created during data processing. These

temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days.

What should you do?

Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

Create a Cloud Run function that runs daily and deletes files older than seven days.

6. You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud.

What should you do?

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

7. You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types. You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements.

What should you do?

Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.

Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.

Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.

Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.

8. 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?

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

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

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

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

9. 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?

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.

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

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.

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.

10. 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 to create a reusable framework in case you need to share this data with other teams in the future.

What should you do?

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

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

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

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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11. 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?

Create a single-region bucket with Autoclass enabled.

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

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

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

12. You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege.

What should you do?

Request the Dataflow Developer role.

Request the Dataflow Viewer role.

Request the Dataflow Worker role.

Request the Dataflow Admin role.

13. BigQuery

14. Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard.

What should you do?

Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.

Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

15. 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?

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

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.

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.

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

16. You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution.

What should you do?

Create a sales_region user attribute, and assign each manager’s region as the value of their user attribute. Add an access_filter Explore filter on the region_name dimension by using the sales_region user attribute.

Create five different Explores with the sql_always_filter Explore filter applied on the region_name dimension. Set each region_name value to the corresponding region for each manager.

Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

17. You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery.

Which data manipulation methodology should you choose?

ELT

ETL

ETLT

18. 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?

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

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

Use 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.

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

19. Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys.

What should you do?

Use Google-managed encryption keys (GMEK).

Use customer-managed encryption keys (CMEK).

Use customer-supplied encryption keys (CSEK).

Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

20. You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach.

What should you do?

Create a partition by transaction date, and set the partition expiration policy to seven years.

Set the table-level retention policy in BigQuery to seven years.

Set the dataset-level retention policy in BigQuery to seven years.

Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

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21. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report.

What should you do?

Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

22. 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?

Migrate the Spark jobs to Dataproc Serverless.

Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

Migrate the Spark jobs to Dataproc on Compute Engine.

23. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance.

What should you do?

Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

24. 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?

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

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.

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

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

25. 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?

Use Cloud Scheduler to schedule the jobs to run.

Use Cloud Tasks to schedule and run the jobs asynchronously.

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

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.

Google Associate Data Practitioner Exam Free Dumps

Google Associate Data Practitioner Exam Free Dumps

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