A fully updated 2026 Associate-Data-Practitioner Exam Dumps exam guide from training expert BraindumpsVCE
Provides complete coverage of every objective on exam and exam preparation Associate-Data-Practitioner
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?
- A. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
- B. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
- C. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- D. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.
NEW QUESTION # 65
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?
- A. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.
- B. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.
- C. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.
- D. 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.
Answer: C
Explanation:
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.
NEW QUESTION # 66
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?
- A. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
- B. Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.
- C. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
- D. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
Answer: A
Explanation:
Creating aBigQuery Cloud resource connectionto a remote model inVertex AIand using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.
NEW QUESTION # 67
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?
- A. Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.
- B. Create a single-region bucket with Archive as the default storage class.
- C. Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.
- D. Create a single-region bucket with Autoclass enabled.
Answer: D
Explanation:
The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing over time, with some files remaining popular. Google Cloud Storage offers tailored options for this:
* Option A: Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30 days, Archive after 90 days) are effective but static. They don't adapt to actual usage, so popular old files in Archive would incur high retrieval costs.
* Option B: Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive) based on object access patterns, not just age. It keeps frequently accessed files in Standard (low latency
/cost for access) and moves inactive ones to cheaper classes, minimizing costs while preserving simplicity. This fits the "some files remain popular" nuance.
* Option C: A Cloud Scheduler job to manually change classes daily is complex (requires scripting, monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM.
NEW QUESTION # 68
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. ETLT
- B. EL
- C. ETL
- D. ELT
Answer: C
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 # 69
You work for a financial services company that handles highly sensitive dat a. 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?
- A. Use customer-supplied encryption keys (CSEK).
- B. Use customer-managed encryption keys (CMEK).
- C. Use a dedicated third-party key management system (KMS) chosen by the company.
- D. Use Google-managed encryption keys (GMEK).
Answer: A
Explanation:
For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures that your organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.
NEW QUESTION # 70
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?
- A. Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.
- B. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
- C. Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.
- D. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.
NEW QUESTION # 71
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?
- A. Use Google-managed encryption keys (GMEK).
- B. Use customer-supplied encryption keys (CSEK).
- C. Use customer-managed encryption keys (CMEK).
- D. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
Answer: A
Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
NEW QUESTION # 72
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?
- A. Change the bucket storage class to Archive.
- B. Enable Object Versioning.
- C. Set temporary object holds.
- D. Set a bucket retention policy.
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Bucket retention policies are specifically designed to enforce a minimum retention period for objects within a Cloud Storage bucket. This ensures that data cannot be deleted or overwritten before the specified period.
Why other options are incorrect:A: Object versioning allows you to keep multiple versions of an object, but it doesn't guarantee a minimum retention period.
B: Changing the storage class to Archive is for cost optimization, not data retention enforcement.
D: Object holds are for legal holds, not general retention.
NEW QUESTION # 73
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 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.
- 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 Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data 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
Answer: C
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 # 74
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?
- A. Enable automatic backups, and create a read replica of the Cloud SQL instance.
- B. Migrate the database to a larger Cloud SQL instance.
- C. Store frequently accessed data in a Memorystore instance.
- D. Use Cloud CDN to cache frequently accessed data.
Answer: A
Explanation:
Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.
NEW QUESTION # 75
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?
- A. Update dataset privileges by using the SQL GRANT statement.
- B. Export the data to Cloud Storage, and use signed URLs to authorize access.
- C. Enable access control by using IAM roles.
- D. Encrypt the data by using customer-managed encryption keys (CMEK).
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
BigQuery uses IAM for access control, adhering to least privilege by granting only necessary permissions.
* Option A: IAM roles (e.g., roles/bigquery.dataViewer for read-only) restrict query access to authorized users, aligning with Google's security best practices.
* Option B: BigQuery doesn't support SQL GRANT for dataset privileges; access is managed via IAM or authorized views.
* Option C: Exporting to Cloud Storage with signed URLs bypasses BigQuery's native controls and adds complexity.
NEW QUESTION # 76
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
- A. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
- B. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- C. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
- D. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Composer is a managed Apache Airflow service, which is a popular open-source workflow orchestration tool.
DAGs in Airflow can be used to automate ETL pipelines.
Airflow's web interface and Cloud Monitoring provide comprehensive monitoring capabilities.
It also allows you to run existing shell scripts.
Why other options are incorrect:B: Dataflow requires rewriting the ETL pipeline using its SDK.
C: Dataproc is for big data processing, not orchestration.
D: Cloud Run functions are for stateless applications, not long-running ETL pipelines.
NEW QUESTION # 77
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 ## of data to Cloud Storage securely, and only have a few days to move the data. What should you do?
- 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 Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
- D. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
Answer: A
Explanation:
UsingTransfer Appliancesis the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.
NEW QUESTION # 78
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?
- A. Request the Dataflow Admin role.
- B. Request the Dataflow Developer role.
- C. Request the Dataflow Worker role.
- D. Request the Dataflow Viewer role.
Answer: B
Explanation:
TheDataflow Developerrole provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as itgrants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access.
Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.
NEW QUESTION # 79
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?
- A. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- B. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
- C. 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.
- D. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
Answer: C
Explanation:
Using the Looker Scheduler with user attribute filters is the Google-recommended approach to efficiently automate the delivery of a customized dashboard. User attribute filters allow you to dynamically customize the dashboard's content based on the recipient's attributes, ensuring each stakeholder sees data relevant to them. This approach is scalable, does not require creating separate models or custom scripts, and leverages Looker's built-in functionality to automate recurring deliveries effectively.
NEW QUESTION # 80
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?
- A. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
- B. Use Cloud Client Libraries to transfer the data over the internet.
- C. Use the gcloud storage command to transfer the data over the internet.
- D. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Transferring 300 TB over a 100 Mbps connection would take an impractical amount of time (over 300 days at theoretical maximum speed, ignoring real-world constraints like latency). Google Cloud provides the Transfer Appliance for large-scale, time-sensitive transfers.
* Option A: Cloud Client Libraries over the internet would be slow and unreliable for 300 TB due to bandwidth limitations.
* Option B: The gcloud storage command is similarly constrained by internet speed and not designed for such large transfers.
* Option C: Compressing and splitting across multiple providers adds complexity and isn't a Google- supported method for Cloud Storage ingestion.
NEW QUESTION # 81
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?
- A. Apply a filter to only show products with a positive profit margin.
- B. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
- C. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
- D. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
Answer: C
Explanation:
Defining a newmeasurein LookML to calculate the profit margin using the existing revenue and cost fields is the most efficient and straightforward solution. This approach allows you to dynamically compute the profit margin directly within your Looker Explores and dashboards without needing to pre-calculate or create additional tables. The measure can be defined using LookML syntax, such as:
measure: profit_margin {
type: number
sql: (revenue - cost) / revenue ;;
value_format: "0.0%"
}
This method is quick to implement and integrates seamlessly into your existing Looker model, enabling accurate visualization of profit margins across your products.
NEW QUESTION # 82
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?
- A. Create a scheduled query to calculate and store intermediate results.
- B. Modify the schema to use parameterized data types.
- C. Disable BiqQuery query result caching.
- D. Create materialized views.
Answer: D
Explanation:
Creatingmaterialized viewsis the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.
For near real-time dashboards with slow queries involving joins and aggregations, Google recommends materialized views in BigQuery to precompute and refresh results automatically.
* Option A: Scheduled queries compute intermediate results periodically, but they aren't near real-time and require manual scheduling, adding latency.
* Option B: Parameterized data types don't address query performance for joins/aggregations; they're for query flexibility, not optimization.
* Option C: Disabling caching worsens performance by forcing full recomputation each time, contrary to the goal.
NEW QUESTION # 83
You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?
- A. ELT; Cloud SQL -> Analytics Hub
- B. ELT; Cloud Storage -> Bigtable
- C. ETL; Dataflow -> BigQuery
- D. ETL; Cloud Data Fusion -> Cloud Storage
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Streaming data requiring enrichment and cleaning before ML training suggests an ETL (Extract, Transform, Load) approach, with a focus on real-time processing and a data warehouse for ML.
* Option A: ETL with Dataflow (streaming transformations) and BigQuery (storage/ML training) is Google's recommended pattern for streaming pipelines. Dataflow handles enrichment/cleaning, and BigQuery supports ML model training (BigQuery ML).
* Option B: ETL with Cloud Data Fusion to Cloud Storage is batch-oriented and lacks streaming focus.
Cloud Storage isn't ideal for ML training directly.
* Option C: ELT (load then transform) with Cloud Storage to Bigtable is misaligned-Bigtable is for NoSQL, not ML training or post-load transformation.
NEW QUESTION # 84
......
Tested Material Used To Associate-Data-Practitioner: https://freetorrent.braindumpsvce.com/Associate-Data-Practitioner_exam-dumps-torrent.html