Exam Associate-Data-Practitioner Realistic Dumps Verified Questions Free [Jan 28, 2026]
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Google Associate-Data-Practitioner Exam Syllabus Topics:
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NEW QUESTION # 32
Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis. What should you recommend?
- A. Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.
- B. Create a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.
- C. Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.
- D. Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.
Answer: A
Explanation:
Using a Colab Enterprise notebook connected to BigQuery provides a managed, collaborative environment ideal for complex statistical calculations, Python packages, and visualizations. Colab Enterprise supports Python libraries for advanced analytics and offers seamless integration with BigQuery for querying large datasets. It allows teams to collaboratively develop and share analyses while taking advantage of its visualization capabilities. This approach is particularly suitable for tasks involving sophisticated computations and custom visualizations.
NEW QUESTION # 33
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Cost minimization requires matching storage classes to access patterns using lifecycle rules. Let's assess:
* Option A: Nearline during training (frequent access) incurs high retrieval costs and latency, unsuitable for ML workloads. Coldline after 30 days and Archive after 90 days are reasonable but misaligned initially.
* Option B: Standard storage (no retrieval fees, low latency) is ideal for frequent access during training.
Transitioning to Nearline (30-day minimum, low access) after 30 days and Coldline (90-day minimum, rare access) after 90 days matches the pattern and minimizes costs effectively.
* Option C: Nearline during training is costly for frequent access, and Archive to Coldline is illogical (Archive is cheaper than Coldline).
NEW QUESTION # 34
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?
- A. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
- B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute. - C. Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.
- D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
Answer: B
Explanation:
Using asales_region user attributeis the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding anaccess_filterExplore filter on theregion_namedimension that references thesales_regionuser attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.
NEW QUESTION # 35
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?
- A. ETL
- B. ETLT
- C. EL
- D. ELT
Answer: A
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
NEW QUESTION # 36
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?
- A. Set the table-level retention policy in BigQuery to seven years.
- B. Set the dataset-level retention policy in BigQuery to seven years.
- C. Create a partition by transaction date, and set the partition expiration policy to seven years.
- D. Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.
Answer: A
Explanation:
Setting a table-level retention policy in BigQuery to seven years is the most efficient and cost-optimized solution to meet the regulatory requirement. A table-level retention policy ensures that the data cannot be deleted or overwritten before the specified retention period expires, providing compliance with auditing requirements while keeping the data within BigQuery for easy access and analysis. This approach avoids the complexity and additional costs of exporting data to Cloud Storage.
NEW QUESTION # 37
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.
- B.
- C.
- D.
Answer: D
Explanation:
To calculate the weekly moving average of sales by location:
* The query must group bystore_id(partitioning the calculation by each store).
* TheORDER BY dateensures the sales are evaluated chronologically.
* TheROWS BETWEEN 6 PRECEDING AND CURRENT ROWspecifies a rolling window of 7 rows (1 week if each row represents daily data).
* TheAVG(total_sales)computes the average sales over the defined rolling window.
Chosen querymeets these requirements:
PARTITION BY store_idgroups the calculation by each store.
ORDER BY dateorders the rows correctly for the rolling average.
ROWS BETWEEN 6 PRECEDING AND CURRENT ROWensures the 7-day moving average.
Extract from Google Documentation: From "Analytic Functions in BigQuery" (https://cloud.google.com
/bigquery/docs/reference/standard-sql/analytic-function-concepts):"Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id."
NEW QUESTION # 38
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?
- A. 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.
- B. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
- C. 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.
- D. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.
* Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
* Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
* Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.
NEW QUESTION # 39
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?
- A. Create a read replica in the same region but in a different zone.
- B. 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.
- C. Create a read replica in another region. Promote the replica to primary if a failure occurs.
- D. 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.
Answer: B
Explanation:
Configuring a high-availability (HA) Cloud SQL instance ensures continuous operation, minimizes downtime, and prevents data loss in the event of a zonal outage. In this setup, the primary instance is located in one zone (e.g., zone A), and a synchronous secondary instance is located in a different zone within the same region. This configuration ensures that all data is replicated to the secondary instance in real-time. In the event of a failure in the primary zone, the system automatically promotes the secondary instance to primary, ensuring seamless failover with no data loss and minimal downtime. This is the recommended approach for mission-critical, highly available databases.
NEW QUESTION # 40
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?
- A. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
- B. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
- C. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
- D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
Answer: B
Explanation:
Using a Cloud Run function triggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.
NEW QUESTION # 41
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?
- A. Store the data in a multi-region bucket.
- B. Store the data in Nearline storaqe.
- C. Set up a Cloud CDN in front of the bucket.
- D. Enable Object Versioning on the bucket.
Answer: A
Explanation:
Storing the data in amulti-region bucketensures high availability and durability, even in the event of a single- zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.
A single-zone outage requires high availability across zones or regions. Cloud Storage offers location-based redundancy options:
* Option A: Cloud CDN caches content for web delivery but doesn't protect against underlying storage outages-it's for performance, not availability of the source data.
* Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn't ensure availability during a zone failure (still tied to one location).
* Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.
NEW QUESTION # 42
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?
- A. Use Dataform to define the transformations in SQLX.
- B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
- C. Create BigQuery scheduled queries to define the transformations in SQL.
- D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
Answer: A
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataform is a managed data transformation service that allows you to define data pipelines using SQL and JavaScript.
It provides version control, modular code development, and data quality checks.
Why other options are incorrect:A: Cloud Composer is an orchestration tool, not a data transformation tool.
B: Scheduled queries are not suitable for complex ETL pipelines.
D: Dataproc requires setting up a Spark cluster and writing code, which is more complex than using Dataform.
NEW QUESTION # 43
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. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
- B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
- C. Migrate the Spark jobs to Dataproc on Compute Engine.
- D. Migrate the Spark jobs to Dataproc Serverless.
Answer: D
Explanation:
Migrating the Spark jobs toDataproc Serverlessis 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.
NEW QUESTION # 44
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.
Answer: 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.
NEW QUESTION # 45
Your company is setting up an enterprise business intelligence platform. You need to limit data access between many different teams while following the Google-recommended approach. What should you do first?
- A. Create a Looker (Google Cloud core) instance, and configure different Looker groups for each team.
- B. Create one Looker Studio report with multiple pages, and add each team's data as a separate data source to the report.
- C. Create a separate Looker Studio report for each team, and share each report with the individuals within each team.
- D. Create a Looker (Google Cloud core) instance, and create a separate dashboard for each team.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
For an enterprise BI platform with data access control across teams, Google recommends Looker (Google Cloud core) over Looker Studio for its robust access management. The "first" step focuses on setting up the foundation.
* Option A: Looker Studio reports are lightweight but lack granular access control beyond sharing.
Creating separate reports per team is inefficient and unscalable.
* Option B: One Looker Studio report with multiple pages and data sources doesn't enforce team-level access control natively-users could access all pages/data.
* Option C: Creating a Looker instance with separate dashboards per team is a step forward but skips the foundational access control setup (groups), reducing scalability.
NEW QUESTION # 46
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?
- 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 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
- C. 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.
- D. 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.
Answer: A
Explanation:
UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time.
It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
NEW QUESTION # 47
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