Improve Your Knowledge with Associate Data Practitioner Exam Dumps

Category:

Comments:

0 Comments

Post Date:

March 10, 2025

Practicing with Associate Data Practitioner questions can help you identify areas where you need to improve your knowledge. By answering Associate Data Practitioner questions and reviewing your responses, you can identify gaps in your understanding and focus your study efforts on those areas. The Associate Data Practitioner exam has a strict time limit, and you need to manage your time effectively to answer all the questions. Practicing with Google Associate Data Practitioner dumps questions can help you develop time management skills by simulating the exam's time constraints. You'll learn how to pace yourself, manage your time effectively, and ensure that you complete the Associate Data Practitioner exam within the allotted time. Test Google Associate Data Practitioner exam free dumps below.

Page 1 of 4

1. 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

2. You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost.

What should you do?

Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

3. You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage.

What should you do?

Set up a Cloud CDN in front of the bucket.

Enable Object Versioning on the bucket.

Store the data in a multi-region bucket.

Store the data in Nearline storaqe.

4. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach.

What should you do?

Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.

Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

5. 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?

Use a Transfer Appliance.

Use Cloud Storage FUS

Use Storage Transfer Service.

Use gcloud storage commands.

6. Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently.

What should you do?

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Apply a filter to only show products with a positive profit margin.

7. 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?

Use customer-supplied encryption keys (CSEK).

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

Use Google-managed encryption keys (GMEK).

Use customer-managed encryption keys (CMEK).

8. 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.

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. You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost-effective, fully-managed, serverless solution to build ELT processes with SQL pipelines. Your solution must include source code control, environment parameterization, and data quality checks.

What should you do?

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

Use Dataform to build, orchestrate, and monitor the pipelines.

Use Dataproc to run MapReduce jobs for distributed data processing.

Use Cloud Composer to orchestrate and run data workflows.

Page 2 of 4

11. 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.

12. 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?

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

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.

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

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

13. Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team’s Python skills.

What should you do?

Use Dataform with assertions.

Deploy Cloud Data Fusion and included plugins.

Use Cloud Composer with pre-built operators.

Use Dataflow and pre-built templates.

14. 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?

Disable BiqQuery query result caching.

Modify the schema to use parameterized data types.

Create a scheduled query to calculate and store intermediate results.

Create materialized views.

15. Create five subfolders inside the Shared folder. Grant each team member the Manage Access, Edit access level to their corresponding subfolder.

16. BigQuery

17. You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region.

What should you do?

Add a policy tag in BigQuery.

Create a row-level access policy.

Create a data masking rule.

Grant the appropriate 1AM permissions on the dataset.

18. 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?

Linear regression

Matrix factorization

Logistic regression

K-means clustering

19. 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?

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

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

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.

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

20. 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 platforms

You 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 MySQ

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 MySQ

Page 3 of 4

21. 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

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

22. 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?

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.

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 SQ

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

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

23. Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly.

What should you do?

Store the data in Cloud Storaqe usinq Nearline storaqe.

Store the data in Cloud Storaqe usinq Coldline storaqe.

Store the data in Cloud Storage using Standard storage.

Store the data in Cloud Storage using Archive storage.

24. 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.

25. 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

26. You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach.

What should you do?

Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

27. 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?

Compare the two different models.

Evaluate the data skewness.

Evaluate data drift.

Compare the confusion matrix.

28. You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission.

What should you do?

Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.

Create a calculated field using the Add a field option in Looker Studio, and add it to your report.

Create a table calculation from the field picker in Looker, and add it to your report.

Create a custom field from the field picker in Looker, and add it to your report.

29. 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 SQL

Use Dataproc to run the transformations on a cluster.

30. You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance.

You want to implement a solution that minimizes effort and cost.

What should you do?

Use Cloud CDN to cache frequently accessed data.

Store frequently accessed data in a Memorystore instance.

Migrate the database to a larger Cloud SQL instance.

Enable automatic backups, and create a read replica of the Cloud SQL instance.

Page 4 of 4

31. 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.

32. You work for a gaming company that collects real-time player activity data. 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?

Use 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.

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

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

Use Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming AP

TAGS:

Associate Data Practitioner, Associate Data Practitioner exam dumps

Notify of

Label

Name*

Email*

Website

Label

Name*

Email*

Website

0 Comments

Oldest

Newest Most Voted

Inline Feedbacks

View all comments

Get Associate Data Practitioner Dumps Full Version

Q&As: 106
Versions: PDF and Software Download Now

About Dumpsinfo

Dumpsinfo is a good platform providing the latest exam information and dumps questions for all IT certification exams. You can study all the latest exam dumps questions online.

[email protected]

Mon - Sat 9:00am - 6:00pm

Improve Your Knowledge with Associate Data Practitioner Exam Dumps

Related

Posts

Get Certified Easily with Online Google Ads Video Dumps

Associate Google Workspace Administrator Online Dumps Boost Your Career

ChromeOS Administrator Dumps Questions – Effective Way to Get Certified

Professional Cloud Database Engineer Exam Dumps – Reliable Way to Pass and Get Certified

About Us

EMAIL

Services

Opening Hours