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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and 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: B

NEW QUESTION # 21
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
B. Create a custom quota for each analyst in BigQuery.
C. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
D. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

Answer: C

NEW QUESTION # 22
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. 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.
B. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
C. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery 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: C

NEW QUESTION # 23
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. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
B. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
C. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
D. 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 I

Answer: B

NEW QUESTION # 24
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Use Cloud Scheduler to schedule the jobs to run.
C. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D. Use Cloud Tasks to schedule and run the jobs asynchronously.

Answer: C

NEW QUESTION # 25
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