Get 100% Success with Latest Google Cloud Platform Associate-Data-Practitioner Exam Dumps May 15, 2026 [Q47-Q65]

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Get 100% Success with Latest Google Cloud Platform Associate-Data-Practitioner Exam Dumps May 15, 2026

The Best Associate-Data-Practitioner Exam Study Material and Preparation Test Question Dumps

Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic	Details
Topic 1	Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Topic 2	Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.\| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 3	Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

NEW QUESTION 47
Your organization’s website uses an on-premises MySQL as a backend database. You need to migrate the on- premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.

Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

NEW QUESTION 48
You work for a global financial services company that trades stocks 24/7. You have a Cloud SGL for PostgreSQL user database. You need to identify a solution that ensures that the database is continuously operational, minimizes downtime, and will not lose any data in the event of a zonal outage. What should you do?

Continuously back up the Cloud SGL instance to Cloud Storage. Create a Compute Engine instance with PostgreSCL in a different region. Restore the backup in the Compute Engine instance if a failure occurs.

Create a read replica in another region. Promote the replica to primary if a failure occurs.

Configure and create a high-availability Cloud SQL instance with the primary instance in zone A and a secondary instance in any zone other than zone A.

Create a read replica in the same region but in a different zone.

NEW QUESTION 49
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google-recommended approach. What should you do?

Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

Embed the Looker dashboard in a custom web application, and use the application’s scheduling features to send the report with personalized filters.

Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.

NEW QUESTION 50
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?

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.

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.

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

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

NEW QUESTION 51
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 custom Object Lifecycle Management policies based on upload date.

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 Archive as the default storage class.

NEW QUESTION 52
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?

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.

Create a view that filters out rows that are older than one year.

Require users to specify a partition filter using the alter table statement in SQL.

Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

NEW QUESTION 53
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team’s existing skillset, processes data at scale, and minimizes cost. What should you do?

Use Dataflow with a custom template for the transformation logic.

Use Cloud Data Fusion to visually design and manage the pipeline.

Use Dataform to define the transformations in SQLX.

Use Dataproc to run the transformations on a cluster.

NEW QUESTION 54
You have a BigQuery dataset containing sales dat
a. 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.

NEW QUESTION 55
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?

ETLT

ETL

ELT

NEW QUESTION 56
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?

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.

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.

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.

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.

NEW QUESTION 57
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users’ personally identifiable information (Pll) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?

1. Cloud Storage
2. CloudSQL for PostgreSQL
3. Bigtable

1. Filestore
2. Cloud SQL for PostgreSQL
3. Datastore

1. Cloud Storage
2. BigQuery
3. Firestore

1. Filestore
2. Bigtable
3. BigQuery

NEW QUESTION 58
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?

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.

NEW QUESTION 59
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

NEW QUESTION 60
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

Use Cloud Logging to identify error groups in the pipeline’s logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.

Use Cloud Logging to view error messages in the pipeline’s logs. Use Cloud Monitoring to analyze the pipeline’s metrics, such as CPU utilization and memory usage.

Use the Dataflow job monitoring interface to check the pipeline’s status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.

NEW QUESTION 61
You need to create a weekly aggregated sales report based on a large volume of dat a. 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.

NEW QUESTION 62
You work for a retail company that collects customer data from various sources:
* Online transactions: Stored in a MySQL database
* Customer feedback: Stored as text files on a company server
* Social media activity: Streamed in real-time from social media platformsYou need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?

Copy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.

Extract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.

Extract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.

Extract and load the online transactions data into Bigtable. Import the customer feedback data into Cloud Storage. Store the social media activity in Cloud SQL for MySQL.

NEW QUESTION 63
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.

Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.

Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.

NEW QUESTION 64
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

NEW QUESTION 65
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

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