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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Insights	20-30%	- Choose appropriate visualization types - Present data insights to stakeholders - Create dashboards and reports - Build visualizations using Looker Studio - Interpret and communicate findings
Topic 2: Data-Driven Decision Making	10-20%	- Define success metrics - Translate business requirements into data solutions - Assess data quality and completeness - Identify stakeholders and requirements
Topic 3: Data Processing and Analytics	20-30%	- Query and analyze datasets - Use BigQuery and SQL for analytics - Aggregate and summarize data - Build and maintain data pipelines - Apply statistical methods for analysis
Topic 4: Data Preparation and Exploration	20-30%	- Ingest and acquire data - Explore data through visualization and queries - Identify data quality issues - Transform and prepare data for analysis - Perform exploratory data analysis (EDA)

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Google Associate Data Practitioner Sample Questions (Q45-Q50):

NEW QUESTION # 45
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
B. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
C. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
D. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

Answer: B

NEW QUESTION # 46
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. Enable access control by using IAM roles.
C. Encrypt the data by using customer-managed encryption keys (CMEK).
D. Export the data to Cloud Storage, and use signed URLs to authorize access.

Answer: B

NEW QUESTION # 47
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
D. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.

Answer: A

NEW QUESTION # 48
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
B. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
C. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
D. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

Answer: A

NEW QUESTION # 49
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 materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
C. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
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: B

NEW QUESTION # 50
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