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 Associate-Data-Practitioner Dumps with Practice Exam Questions Answers

Questions: 72 questions With Step-by-Step Explanation

Last Update: Jan 20, 2025

Associate-Data-Practitioner Question Includes: Single Choice Questions: 72,

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Associate-Data-Practitioner Questions and Answers

Question # 1

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: Cloud Storage

2. Customer feedback: Cloud Storage

3. Social media activity: Cloud Storage

B.

1. Online transactions: BigQuery

2. Customer feedback: Cloud Storage

3. Social media activity: BigQuery

C.

1. Online transactions: Bigtable

2. Customer feedback: Cloud Storage

3. Social media activity: CloudSQL for MySQL

D.

1. Online transactions: Cloud SQL for MySQL

2. Customer feedback: BigQuery

3. Social media activity: Cloud Storage

Question # 2

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.

Use Cloud Scheduler to schedule the jobs to run.

B.

Use Cloud Tasks to schedule and run the jobs asynchronously.

C.

Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

D.

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.

Question # 3

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.

Store frequently accessed data in a Memorystore instance.

C.

Migrate the database to a larger Cloud SQL instance.

D.

Enable automatic backups, and create a read replica of the Cloud SQL instance.

Question # 4

You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

A.

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

B.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

C.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

D.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

Question # 5

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 a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

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.

Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

D.

Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.