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Google Associate-Data-Practitioner Exam Preparation: Google Cloud Associate Data Practitioner - NewPassLeader Brings the best Valid Test Cost with One Year Free Updates

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

Topic 1

Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.

Topic 2

Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

Topic 3

Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.

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Google Cloud Associate Data Practitioner Sample Questions (Q47-Q52):

NEW QUESTION # 47
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. 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.
B. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
C. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
D. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

Answer: B

NEW QUESTION # 48
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 Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B. Use Dataform to define the transformations in SQLX.
C. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
D. Create BigQuery scheduled queries to define the transformations in SQL.

Answer: B

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 # 49
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 a native table.
B. Create an external table.
C. Create a temporary table.
D. Create an object table.

Answer: D

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 # 50
You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
B. Store all data in a single BigQuery table without partitioning or lifecycle policies.
C. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
D. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Answer: A

Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data toColdline storageminimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

NEW QUESTION # 51
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?

Answer: C

Explanation:
To calculate the weekly moving average of sales by location:
The query must group by store_id (partitioning the calculation by each store).
The ORDER BY date ensures the sales are evaluated chronologically.
The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).
The AVG(total_sales) computes the average sales over the defined rolling window.
Chosen query meets these requirements:

NEW QUESTION # 52
......

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