LATEST ASSOCIATE-DATA-PRACTITIONER EXAM PAPERS | NEW ASSOCIATE-DATA-PRACTITIONER TEST VCE FREE

Latest Associate-Data-Practitioner Exam Papers | New Associate-Data-Practitioner Test Vce Free

Latest Associate-Data-Practitioner Exam Papers | New Associate-Data-Practitioner Test Vce Free

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Google Cloud Associate Data Practitioner Sample Questions (Q86-Q91):

NEW QUESTION # 86
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?

  • A. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
  • B. 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.
  • C. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
  • D. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:
* Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post- disaster. Primary operations remain in one zone, minimizing latency.
* Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour-too high for valuable data. Manual backup management adds overhead.
* Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.


NEW QUESTION # 87
Your retail company 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 are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

  • A. 1. Online transactions: BigQuery
    2. Customer feedback: Cloud Storage
    3. Social media activity: BigQuery
  • B. 1. Online transactions: Cloud SQL for MySQL
    2. Customer feedback: BigQuery
    3. Social media activity: Cloud Storage
  • C. 1. Online transactions: Bigtable
    2. Customer feedback: Cloud Storage
    3. Social media activity: CloudSQL for MySQL
  • D. 1. Online transactions: Cloud Storage
    2. Customer feedback: Cloud Storage
    3. Social media activity: Cloud Storage

Answer: A

Explanation:
Online transactions:Storing the transactional data inBigQueryis ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback:Storing customer feedback inCloud Storageis appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity:Storing real-time social media activity inBigQueryis optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.


NEW QUESTION # 88
You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

  • A. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.
  • B. Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.
    PREDICT function call to output the predictions into a new BigQuery table.
  • C. Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.
    PREDICT function call to output the predictions into a new BigQuery table.
  • D. Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.
    FORECAST function call to output the predictions into a new BigQuery table.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let's analyze:
* Option A: BigQuery ML's time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.
* Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML's in-place processing.
* Option C: Linear regression predicts continuous values but doesn't handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.


NEW QUESTION # 89
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) copyright. 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 copyright to summarize the data.
  • B. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the copyright API to summarize the data.
  • C. Query the BigQuery table from within a Python notebook, use the copyright API to summarize the data within the notebook, and store the summaries in BigQuery.
  • D. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the copyright API to summarize the pre- processed data.

Answer: A

Explanation:
Creating aBigQuery Cloud resource connectionto a remote model inVertex AIand using copyright to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the copyright 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 # 90
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

  • A. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
  • B. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
  • C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
  • D. Grant each partner read access to the BigQuery dataset by using 1AM roles.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Analytics Hub allows you to share datasets with external partners while maintaining control over access.
Subscriptions allow granular control.
Why other options are incorrect:B: Cloud storage is for files, not bigquery datasets.
C: IAM roles do not allow for granular row and column level control.
D: Creating a separate project for each partner is complex and not scalable.


NEW QUESTION # 91
......

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