ASSOCIATE-DATA-PRACTITIONER EXAM ENGINE - ASSOCIATE-DATA-PRACTITIONER RELIABLE BRAINDUMPS EBOOK

Associate-Data-Practitioner Exam Engine - Associate-Data-Practitioner Reliable Braindumps Ebook

Associate-Data-Practitioner Exam Engine - Associate-Data-Practitioner Reliable Braindumps Ebook

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Google Cloud Associate Data Practitioner Sample Questions (Q34-Q39):

NEW QUESTION # 34
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. Use Cloud CDN to cache frequently accessed data.
  • B. Migrate the database to a larger Cloud SQL instance.
  • C. Store frequently accessed data in a Memorystore instance.
  • D. Enable automatic backups, and create a read replica of the Cloud SQL instance.

Answer: D

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 # 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. ELT
  • C. ETLT
  • D. EL

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
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

  • A. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
  • B. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
  • C. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job ID.
  • D. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

Answer: B


NEW QUESTION # 37
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

  • A. Create an external table.
  • B. Create an object table.
  • C. Create a temporary table.
  • D. Create a native table.

Answer: B

Explanation:
To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.


NEW QUESTION # 38
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?

  • A. 1. Filestore
    2. Bigtable
    3. BigQuery
  • B. 1. Cloud Storage
    2. BigQuery
    3. Firestore
  • C. 1. Cloud Storage
    2. CloudSQL for PostgreSQL
    3. Bigtable
  • D. 1. Filestore
    2. Cloud SQL for PostgreSQL
    3. Datastore

Answer: B

Explanation:
Audio files from phone interactions: Use Cloud Storage. Cloud Storage is ideal for storing large binary objects like audio files, offering scalability and easy accessibility for training purposes.
CSV files of users' personally identifiable information (PII): Use BigQuery. BigQuery is a serverless data warehouse optimized for analyzing structured data, such as CSV files, using SQL. It ensures compliance with PII handling through access controls and data encryption.
A large volume of small document files: Use Firestore. Firestore is a scalable NoSQL database designed for applications requiring fast, real-time interactions and structured document storage, making it suitable for powering other applications.


NEW QUESTION # 39
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