A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Choose two.)
A. Configure the third-party application to create the files in a columnar format.
B. Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.
C. Partition the order data in the S3 bucket based on order date.
D. Configure the third-party application to create the files in JSON format.
E. Load the JSON data into the Amazon Redshift table in a SUPER type column.
Show Answer
Correct Answer: A, C
Explanation: Using a columnar format such as Parquet or ORC greatly improves Redshift Spectrum performance because only the required columns are read, reducing scan I/O for queries that select subsets of more than 100 columns. Partitioning the S3 data by order date enables partition pruning so Spectrum scans only the relevant daily partitions for user queries. Converting many CSV files into one larger CSV file does not address the main inefficiencies of row-based storage and offers less benefit than columnar storage plus partitioning.
Question 143
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications? (Choose two.)
A. Gremlin
B. SQL
C. ANSI SQL
D. SPARQL
E. Spark SQL
Show Answer
Correct Answer: A, D
Explanation: Amazon Neptune supports the Gremlin traversal language (for property graph models) and SPARQL (for RDF graph models). It does not use SQL, ANSI SQL, or Spark SQL as its graph query languages.
Question 144
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
A. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.
B. Attach an IAM role to the EKS worker nodes, Grant the IAM role access to DynamoDUse the IAM role to set up IAM roles service accounts (IRSA) functionality.
C. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.
D. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS duster nodes to store the user access key data.
Show Answer
Correct Answer: B
Explanation: Amazon EKS best practice is to use IAM Roles for Service Accounts (IRSA), which allows pods to obtain temporary AWS credentials through an IAM role associated with a Kubernetes service account, avoiding embedded long-term credentials. Option B describes granting the required DynamoDB permissions via an IAM role and using IRSA. The other options rely on storing long-term credentials, which is less secure.
Question 145
A company uses Amazon Redshift as its data warehouse service. A data engineer needs to design a physical data model.
The data engineer encounters a de-normalized table that is growing in size. The table does not have a suitable column to use as the distribution key.
Which distribution style should the data engineer use to meet these requirements with the LEAST maintenance overhead?
A. ALL distribution
B. EVEN distribution
C. AUTO distribution
D. KEY distribution
Show Answer
Correct Answer: C
Explanation: AUTO distribution provides the least maintenance overhead because Amazon Redshift automatically selects and can later change the distribution style as the table grows. Since there is no suitable distribution key and the table is growing, AUTO can ultimately choose EVEN when appropriate without requiring manual intervention. ALL is unsuitable for a large growing table, and KEY is ruled out by the lack of a suitable key.
Question 146
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?
A. Create a separate Redshift cluster for each team. Load only the required data for each team. Restrict access to clusters based on the teams.
B. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
C. Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
D. Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.
Show Answer
Correct Answer: C
Explanation: Amazon Redshift supports dynamic data masking with masking policies that can be attached based on database roles, allowing different teams to see masked or unmasked values without duplicating data. Creating separate roles for marketing, claims, and analytics and applying appropriate masking policies minimizes administrative overhead compared to maintaining separate clusters or multiple views. This keeps access control centralized within the Redshift cluster.
Question 147
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?
A. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
B. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.
C. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
D. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
Show Answer
Correct Answer: A
Explanation: Using Firehose's built-in record format conversion to Apache Parquet reduces Athena scan costs without recreating the ingestion pipeline. Configuring larger buffer sizes creates larger objects, improving query efficiency, and using a date-based S3 prefix enables Athena partitioning. For historical JSON data, a one-time AWS Glue ETL job can convert and compact it into Parquet. The other options introduce unnecessary infrastructure (EMR, Flink, Lambda) or recreate the pipeline, increasing management effort.
Question 148
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
A. Run the ANALYZE command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
B. Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
C. Run the VACUUM REINDEX command against the identified tables.
D. Run the VACUUM RECLUSTER command against the identified tables.
Show Answer
Correct Answer: B
Explanation: ANALYZE COMPRESSION evaluates the data in a table and recommends the optimal compression encoding for each column. After reviewing the recommendations, the engineer must alter or recreate the table/columns to apply the suggested encodings. ANALYZE updates optimizer statistics, not compression encodings, and VACUUM operations reorganize storage rather than recommend or change compression encodings.
Question 149
A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
Which solution will meet this requirement with the LEAST manual intervention?
A. Configure the S3 bucket and S3 objects to allow access to Amazon Macie. Use automated sensitive data discovery in Macie.
B. Configure AWS CloudTrail to monitor S3 PUT operations. Inspect the CloudTrail trails to identify operations that save PII.
C. Create an AWS Lambda function to identify PII in S3 objects. Schedule the function to run periodically.
D. Create a table in AWS Glue Data Catalog. Write custom SQL queries to identify PII in the table. Use Amazon Athena to run the queries.
Show Answer
Correct Answer: A
Explanation: Amazon Macie is purpose-built to automatically discover, classify, and protect sensitive data such as PII in Amazon S3 using managed data discovery. It requires far less manual effort than building custom Lambda logic, inspecting CloudTrail logs, or writing Athena/Glue queries, none of which are designed for automated PII classification.
Question 150
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?
A. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
B. AWS Step Functions
C. AWS Glue
D. Amazon EventBridge
Show Answer
Correct Answer: A
Explanation: Amazon Managed Workflows for Apache Airflow (MWAA) is the best fit because the requirement explicitly states that the orchestration should be implemented in Python. Airflow workflows (DAGs) are authored in Python and can orchestrate Amazon EMR Spark jobs, Salesforce API calls via Python operators/hooks, and Amazon Redshift loads. MWAA also provides scheduling, dependency management, and built-in retry and failure handling. Step Functions has strong orchestration and retry capabilities, but its workflow definitions are state machines (Amazon States Language), not Python-native orchestration.
Question 151
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?
A. Store the custom Python scripts in a shared Amazon S3 bucket. Store a pointer to the custom scripts in the execution context object.
B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
C. Store the custom Python scripts in a shared Amazon S3 bucket. Store a pointer to the customer scripts in environment variables.
D. Assign the same alias to each Lambda function. Call each Lambda function by specifying the function's alias.
Show Answer
Correct Answer: B
Explanation: AWS Lambda layers are designed to package and share common code, libraries, and dependencies across multiple Lambda functions. By placing the shared Python formatting scripts in a Lambda layer, the engineer can update the shared code centrally by publishing a new layer version and associating it with the functions, avoiding duplicated code in every function. The other options do not provide a supported mechanism for sharing and centrally managing reusable code across Lambda functions.
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